i-manager Publications

i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 11 Issue 1

Most Cited

Volume 9 Issue 2 July - December 2022

Research Paper

A Model-Free Gait Recognition Approach for Person Identification with Covariates

Surekha Samsani *

Department of Computer Science and Engineering, University College of Engineering Kakinada (A), JNTUK, Andhra Pradesh, India.

Samsani, S. (2022). A Model-Free Gait Recognition Approach for Person Identification with Covariates. i-manager’s Journal on Pattern Recognition, 9(2), 1-8. https://doi.org/10.26634/jpr.9.2.19124

Abstract

In the present scenario, the identification of abnormal activity in the crowd plays a vital role in the detection of criminal and mental activities. Gait can be utilized for person authentication and can be used in surveillance where a large number of people pass through. Gait recognition is an evolving biometric technology, which involves people being recognized purely through walking manner. It is an interesting research topic as gait can be obtained without a person's cooperation, and gait has a unique nature. At present, the methods being used for gait recognition can be broadly divided into two categories, model-based, and model-free approaches. The model-based approach requires prior modeling to generate gait features, which requires high computational costs, whereas the model-free approach directly extracts features from silhouette frames. The main challenge faced by a model-free approach is that it is sensitive to covariate conditions such as clothing, carrying objects, and walking surfaces. This paper presents a modelfree approach that is very effective at handling known and unknown covariates. In this paper, two popular methods, namely, Convolutional Neural Network (CNN) and discriminative feature-based classification methods, are used for gait recognition. The CNN-based method is used for known covariates, and the discriminative feature-based classification method is used for unknown covariate conditions. The Chinese Academy of Sciences (CASIA) Gait Database B from the Center for Biometrics and Security Research (CBSR) is used to train the models and their performance is evaluated in terms of Accuracy, Precision, Recall, and F1-Scores.

References

[1]. Bouchrika, I. (2018). A survey of using biometrics for smart visual surveillance: Gait recognition. In Surveillance in Action (pp. 3-23). Springer, Cham. https://doi.org/10.1007/978-3-319-68533-5_1

[2]. Chai, Y., Ren, J., Han, W., & Li, H. (2011). Human gait recognition: approaches, datasets and challenges. 4th International Conference on Imaging for Crime Detection and Prevention 2011, https://doi.org/10.1049/ic.2011.0100

[3]. Collins, R. T., Gross, R., & Shi, J. (2002, May). Silhouette-based human identification from body shape and gait. In Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition (pp. 366-371). IEEE. https://doi.org/10.1109/AFGR.2002.1004181

[4]. Connor, P., & Ross, A. (2018). Biometric recognition by gait: A survey of modalities and features. Computer Vision and Image Understanding, 167, 1-27. https://doi.org/10.1016/j.cviu.2018.01.007

[5]. Han, J., & Bhanu, B. (2006). Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2), 316-322. https://doi.org/10.1109/TPAMI.2006.38

[6]. Isaac, E. R., Elias, S., Rajagopalan, S., & Easwarakumar, K. S. (2017). View-invariant gait recognition through genetic template segmentation. IEEE Signal Processing Letters, 24(8), 1188-1192. https://doi.org/10.1109/LSP. 2017.2715179

[7]. Kalsoom, A., Maqsood, M., Ghazanfar, M. A., Aadil, F., & Rho, S. (2018). A dimensionality reduction-based efficient software fault prediction using Fisher linear discriminant analysis (FLDA). The Journal of Supercomputing, 74(9), 4568-4602. https://doi.org/ 10.1007/s11227-018-2326-5

[8]. Lishani, A. O., Boubchir, L., & Bouridane, A. (2014, December). Haralick features for GEI-based human gait recognition. In 2014 26th International Conference on Microelectronics (ICM) (pp. 36-39). IEEE. https://doi.org/10.1109/ICM.2014.7071800

[9]. Lishani, A. O., Boubchir, L., Khalifa, E., & Bouridane, A. (2019). Human gait recognition using GEI-based local multi-scale feature descriptors. Multimedia Tools and Applications, 78(5), 5715-5730. https://doi.org/10.1007/s11042-018-5752-8

[10]. Nithyakani, P., Shanthini, A., & Ponsam, G. (2019, February). Human gait recognition using deep convolutional neural network. In 2019 3rd International Conference on Computing and Communications Technologies (ICCCT) (pp. 208-211). IEEE. https://doi.org/10.1109/ICCCT2.2019.8824836

[11]. Popade, M., & Thengade, A. (2016). Human gait recognition based on gait energy image. International Journal of Science and Research (IJSR), 5(11), 1393-1398.

[12]. Prakash, C., Kumar, R., & Mittal, N. (2018). Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges. Artificial Intelligence Review, 49(1), 1-40. https://doi.org/10.1007/s10462-016-9514-6

[13]. Rida, I., Bouridane, A., Marcialis, G. L., & Tuveri, P. (2015, September). Improved human gait recognition. In International Conference on Image Analysis and Processing (pp. 119-129). Springer, Cham. https://doi.org/10.1007/978-3-319-23234-8_12

[14]. Singh, J. P., Jain, S., Arora, S., & Singh, U. P. (2018). Vision-based gait recognition: A survey. IEEE Access, 6, 70497-70527. https://doi.org/10.1109/ACCESS.2018.2879896

[15]. Singh, J. P., Jain, S., Arora, S., & Singh, U. P. (2021). A survey of behavioral biometric gait recognition: Current success and future perspectives. Archives of Computational Methods in Engineering, 28(1), 107-148. https://doi.org/10.1007/s11831-019-09375-3

[16]. Wan, C., Wang, L., & Phoha, V. V. (Eds.). (2018). A survey on gait recognition. ACM Computing Surveys (CSUR), 51(5), 1-35. https://doi.org/10.1145/3230633

[17]. Yeoh, T., Aguirre, H. E., & Tanaka, K. (2016, October). Clothing-invariant gait recognition using convolutional neural network. In 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) (pp. 1-5). IEEE. https://doi.org/10.1109/ISPACS.2016.7824728

[18]. Zhang, Y., Huang, Y., Wang, L., & Yu, S. (2019). A comprehensive study on gait biometrics using a joint CNN-based method. Pattern Recognition, 93, 228-236. https://doi.org/10.1016/j.patcog.2019.04.023

Full Article (HTML)

Pdf

Research Paper

Weed Identification using Machine Learning Models

Vandana Saini* , Shalini, Krishan Kumar Sharma*

*-*** Department of Computer Science & Informatics, University of Kota, Kota, Rajasthan, India.

Saini, V., Shalini, and Sharma, K. K. (2022). Weed Identification using Machine Learning Models. i-manager’s Journal on Pattern Recognition, 9(2), 9-16. https://doi.org/10.26634/jpr.9.2.19086

Abstract

Image classification is a complex process and an important direction in the field of image processing. Image classification methods require learning and training stages. Using machine learning classification models in image classification gives better results. Decision Tree, Random Forest, Gradient Boosting, Bagging Classifier, Multi-Layer Perceptron (MLP) Classifier, and Support Vector Machine (SVM) are different machine-learning classification models. The goal of this paper is to analyze the machine learning classification models. These models classify 12 kinds of plant seedlings, of which 3 are crop seedlings and 9 are weed seedlings. This paper suggests that, when using a V2 Plant Seedlings dataset, the accuracy of SVM is 0.71 and the accuracy of other models is less compared to SVM. The experimental results in this paper show that the machine learning model SVM has a better solution effect and higher recognition accuracy. This paper focuses on model building, training, and assessing the quality of the model by generating a confusion matrix and a classification report.

References

[1]. Ahmed, F., Al-Mamun, H. A., Bari, A. H., Hossain, E., & Kwan, P. (2012). Classification of crops and weeds from digital images: A support vector machine approach. Crop Protection, 40, 98-104. https://doi.org/10.1016/j.cropro.2012.04.024

[2]. Alam, M., Alam, M. S., Roman, M., Tufail, M., Khan, M. U., & Khan, M. T. (2020, April). Real-time machine-learning based crop/weed detection and classification for variable-rate spraying in precision agriculture. In 2020, 7th International Conference on Electrical and Electronics Engineering (ICEEE) (pp. 273-280). IEEE. https://doi.org/10.1109/ICEEE49618.2020.9102505

[3]. Bakhshipour, A., & Jafari, A. (2018). Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Computers and Electronics in Agriculture, 145, 153-160. https://doi.org/10.1016/j.compag.2017.12.032

[4]. Bhatkar, A. P., &Kharat, G. U. (2015, December). Detection of diabetic retinopathy in retinal images using MLP classifier. In 2015, IEEE International Symposium on Nanoelectronic and Information Systems (pp. 331-335). IEEE. https://doi.org/10.1109/iNIS.2015.30

[5]. Bini, D., Pamela, D., & Prince, S. (2020, March). Machine vision and machine learning for intelligent agrobots: A review. In 2020 5th International Conference on Devices, Circuits and Systems (ICDCS) (pp. 12-16). IEEE. https://doi.org/10.1109/ICDCS48716.2020.243538

[6]. Burkart, N., & Huber, M. F. (2021). A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70, 245-317. https://doi.org/10.1613/jair.1.12228

[7]. Chauhan, N. K., & Singh, K. (2018, September). A review on conventional machine learning vs deep learning. In 2018, International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 347-352). IEEE. https://doi.org/10.1109/GUCON.2018.8675097

[8]. Dokic, K., Blaskovic, L., &Mandusic, D. (2020, December). From machine learning to deep learning in agriculture–the quantitative review of trends. In IOP Conference Series: Earth and Environmental Science, 614(1), 012138. IOP Publishing. https://doi.org/10.1088/1755-1315/614/1/012138

[9]. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc..

[10]. Hamuda, E., Glavin, M., & Jones, E. (2016). A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, 125, 184-199. https://doi.org/10.1016/j.compag.2016.04.024

[11]. Hemming, J., & Rath, T. (2001). PA—Precision agriculture: Computer-vision-based weed identification under field conditions using controlled lighting. Journal of Agricultural Engineering Research, 78(3), 233-243. https://doi.org/10.1006/jaer.2000.0639

[12]. Humeau-Heurtier, A. (2019). Texture feature extraction methods: A survey. IEEE Access, 7, 8975-9000. https://doi.org/10.1109/Access.2018.2890743

[13]. Islam, N., Rashid, M. M., Wibowo, S., Xu, C. Y., Morshed, A., Wasimi, S. A., ...& Rahman, S. M. (2021). Early weed detection using image processing and machine learning techniques in an Australian chilli farm. Agriculture, 11(5), 387. https://doi.org/10.3390/agriculture11050387

[14]. Jabir, B., Falih, N., Sarih, A., & Tannouche, A. (2021). A strategic analytics using convolutional neural networks for weed identification in sugar beet fields. AGRIS on-line Papers in Economics and Informatics, 13(665-2022-448), 49-57. https://doi.org/10.22004/ag.econ.320247

[15]. Júnior, P. C. P., Monteiro, A., Da Luz Ribeiro, R., Sobieranski, A. C., & Von Wangenheim, A. (2020). Comparison of supervised classifiers and image features for crop rows segmentation on aerial images. Applied Artificial Intelligence, 34(4), 271-291. https://doi.org/10.1080/08839514.2020.1720131

[16]. Kazmi, W., Garcia-Ruiz, F., Nielsen, J., Rasmussen, J., & Andersen, H. J. (2015). Exploiting affine invariant regions and leaf edge shapes for weed detection. Computers and Electronics in Agriculture, 118, 290-299. https://doi.org/10.1016/j.compag.2015.08.023

[17]. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674

[18]. Mosavi, A., Ozturk, P., & Chau, K. W. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536

[19]. Sharma, P., Hans, P., & Gupta, S. C. (2020, January). Classification of plant leaf diseases using machine learning and image preprocessing techniques. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 480-484). IEEE. https://doi.org/10.1109/Confluence47617.2020.9057889

[20]. Tian, L., Slaughter, D. C., & Norris, R. F. (2000). Machine vision identification of tomato seedlings for automated weed control. Transactions of ASAE, 40(6), 1761-1768.

[21]. Wang, A., Zhang, W., & Wei, X. (2019). A review on weed detection using ground-based machine vision and image processing techniques. Computers and Electronics in Agriculture, 158, 226-240. https://doi.org/10.1016/j.compag.2019.02.005

[22]. Wang, C., Wang, J., Du, Q., & Yang, X. (2020, December). Dog breed classification based on deep learning. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 209-212). IEEE. https://doi.org/10.1109/ISCID51228.2020. 00053

[23]. Wang, P., Fan, E., & Wang, P. (2021). Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 141, 61-67. https://doi.org/10.1016/j.patrec.2020.07.042

[24]. Wendel, A., & Underwood, J. (2016, May). Selfsupervised weed detection in vegetable crops using ground based hyperspectral imaging. In 2016, IEEE International Conference on Robotics and Automation (ICRA) (pp. 5128-5135). IEEE. https://doi.org/10.1109/ICRA.2016.7487717

Full Article (HTML)

Pdf

Review Paper

Review on Various Face Recognition Databases

Khushi Bhoj* , Kuldeep Choksi, Rishi Kitawat*, Manish Rana****

*-**** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, India.

Bhoj, K., Choksi, K., Kitawat, R., and Rana, M. (2022). Review on Various Face Recognition Databases. i-manager’s Journal on Pattern Recognition, 9(2), 17-29. https://doi.org/10.26634/jpr.9.2.19189

Abstract

Face recognition is one of the multimedia items that has seen a remarkable increase in popularity in recent years. Face continues to be the most difficult study topic for experts in the field of computer vision and image processing since it is an item with different properties for detection. We have attempted to handle the most challenging facial aspects in this survey work, including posture invariance, aging, illuminations, and partial occlusion. When applied to facial photographs, they are regarded as essential components of face recognition systems. The most recent face detection methods and techniques are also examined in this paper, including Eigenface, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, Elastic Bunch Graph Matching, 3D Morphable Models, and Hidden Markov Models. Many testing face databases, such as AT & T (ORL), AR, FERET, LFW, YTF, and Yale, also reviewed. However, the purpose of this study is to present a thorough literature assessment on face recognition and its applications.

References

[1]. Abdullah, M., Wazzan, M., & Bo-Saeed, S. (2012). Optimizing face recognition using PCA. International Journal of Artificial Intelligence & Applications, 3(2), 23-31. https://doi.org/10.5121/ijaia.2012.3203

[2]. Agrawal, S., & Khatri, P. (2015, February). Facial expression detection techniques: based on Viola and Jones algorithm and principal component analysis. In 2015 Fifth International Conference on Advanced Computing & Communication Technologies (pp. 108-112). IEEE. https://doi.org/10.1109/ACCT.2015.32

[3]. Anand, B., & Shah, P. K. (2016). Face recognition using SURF features and SVM classifier. International Journal of Electronics Engineering Research, 8(1), 1-8.

[4]. Azeem, A., Sharif, M., Raza, M., & Murtaza, M. (2014). A survey: Face recognition techniques under partial occlusion. International Arab Journal of Information Technology, 11(1), 1-10.

[5]. Bellakhdhar, F., Loukil, K., & Abid, M. (2013). Face recognition approach using Gabor wavelets, PCA and SVM. International Journal of Computer Science Issues (IJCSI), 10(2), 201-206.

[6]. Best-Rowden, L., & Jain, A.K. (2015). A longitudinal study of automatic face recognition. In 2015 International Conference on Biometrics (ICB), 214-221. https://doi.org/10.1109/ICB.2015.7139087

[7]. Chai, X., Shan, S., & Gao, W. (2003, December). Pose normalization for robust face recognition based on statistical affine transformation. In Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint, 3, 1413-1417. IEEE. https://doi.org/10.1109/ICICS.2003.1292698

[8]. Chen, C., Liu, K., & Kehtarnavaz, N. (2016). Real-time human action recognition based on depth motion maps. Journal of Real-Time Image Processing, 12(1), 155-163. https://doi.org/10.1007/s11554-013-0370-1

[9]. Cho, H., Roberts, R., Jung, B., Choi, O., & Moon, S. (2014). An efficient hybrid face recognition algorithm using PCA and GABOR wavelets. International Journal of Advanced Robotic Systems, 11(4), 1-8. https://doi.org/10.5772/58473

[10]. Gorde, M. S. H., & Singh, M. M. K. (2017). A review on face recognition algorithms. Asian Journal for Convergence in Technology (AJCT), 3(1).

[11]. Huang, F. J., Zhou, Z., Zhang, H. J., & Chen, T. (2000, March). Pose invariant face recognition. In Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) (pp. 245-250). IEEE. https://doi.org/10.1109/AFGR.2000.840642

[12]. Huang, J., Blanz, V., & Heisele, B. (2002, August). Face recognition using component-based SVM classification and morphable models. In International Workshop on Support Vector Machines (pp. 334-341). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_26

[13]. Jazouli, M., Majda, A., & Zarghili, A. (2017, April). A $ P recognizer for automatic facial emotion recognition using Kinect sensor. In 2017 Intelligent Systems and Computer Vision (ISCV) (pp. 1-5). IEEE. https://doi.org/10.1109/ISACV.2017.8054955

[14]. Jia, H., & Martinez, A. M. (2008, September). Face recognition with occlusions in the training and testing sets. In 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition (pp. 1-6). IEEE. https://doi.org/10.1109/AFGR.2008.4813410

[15]. Jin, Y., & Ruan, Q. Q. (2009). Face recognition using gabor-based improved supervised locality preserving projections. Computing and Informatics, 28(1), 81-95.

[16]. Kar, A., Bhattacharjee, D., Nasipuri, M., Basu, D. K., & Kundu, M. (2009). Classification of high-energized Gabor responses using bayesian PCA for human face recognition. International Journal of Recent Trends in Engineering, 2(2), 106.

[17]. Kong, R., & Zhang, B. (2011). A new face recognition method based on fast least squares support vector machine. Physics Procedia, 22, 616-621. https://doi.org/10.1016/j.phpro.2011.11.095

[18]. Le, T. H., & Bui, L. (2011). Face recognition based on SVM and 2DPCA. arXiv preprint arXiv:1110.5404. https://doi.org/10.48550/arXiv.1110.5404

[19]. Li, Y., Gong, S., Sherrah, J., & Liddell, H. (2000, September). Multi-view face detection using support vector machines and eigenspace modelling. In KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No. 00TH8516), 1, 241-244. IEEE. https://doi.org/10.1109/KES.2000.885802

[20]. Liu, X., & Chen, T. (2003, June). Video-based face recognition using adaptive hidden markov models. In Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'03), 1, 340–345. https://doi.org/10.1109/CVPR.2003.1211373

[21]. Miarnaeimi, H., & Davari, P. (2008). A new fast and efficient HMM-based face recognition system using a 7- state HMM along with SVD coefficients. Iranian Journal of Electrical and Electronic Engineering, 4(1), 46-57.

[22]. Ming, Y., Ruan, Q., & Wang, X. (2012). Efficient 3d face recognition with Gabor patched spectral regression. Computing and Informatics, 31(4), 779-803.

[23]. Nandini, M., Bhargavi, P., & Sekhar, G. R. (2013). Face recognition using neural networks. International Journal of Scientific and Research Publications, 3(3), 1-5.

[24]. Nefian, A. V., & Hayes, M. H. (1998, May). Hidden Markov models for face recognition. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP'98 (Cat. No. 98CH36181), 5, 2721-2724. https://doi.org/10.1109/ICASSP.1998.678085

[25]. Prakash, N. K. (2010). Face detection using neural network. International Journal of Computer Applications, 1(14), 36–39. https://doi.org/10.5120/345-524

[26]. Raja, A. S., & JosephRaj, V. (2012). Neural network based supervised self-organizing maps for face recognition. International Journal on Soft Computing, 3(3), 31-39. https://doi.org/10.5121/ijsc.2012.3303

[27]. Shah, J. H., Sharif, M., Raza, M., & Azeem, A. (2014). Face recognition across pose variation and the 3S problem. Turkish Journal of Electrical Engineering and Computer Sciences, 22(6), 1423-1436. https://doi.org/10.3906/elk-1108-70

[28]. Sharif, M., Ali, M. A., Raza, M., & Mohsin, S. (2015). Face recognition using edge information and DCT. Sindh University Research Journal-SURJ (Science Series), 43(2), 209- 214.

[29]. Sharif, M., Anis, S., Raza, M., & Mohsin, S. (2012). Enhanced SVD based face recognition. Journal of Applied Computer Science & Mathematics, (12), 49-53.

[30]. Sharif, M., Ayub, K., Sattar, D., & Raza, M. (2012). Real time face detection. Sindh University Research Journal-SURJ (Science Series), 44(4), 597- 600.

[31]. Sharif, M., Mohsin, S., Hanan, R. A., Javed, M. Y., & Raza, M. (2011). 3d face recognition using horizontal and vertical marked strips. Sindh University Research Journal-SURJ (Science Series), 43(1), 57-62.

[32]. Sharif, M., Mohsin, S., Jamal, M. J., & Raza, M. (2010, July). Illumination normalization preprocessing for face recognition. In 2010 the 2nd Conference on Environmental Science and Information Application Technology, 2, 44-47. IEEE. https://doi.org/10.1109/ESIAT. 2010.5567274

[33]. Sharif, M., Naz, F., Yasmin, M., Shahid, M. A., & Rehman, A. (2017). Face recognition: A survey. Journal of Engineering Science & Technology Review, 10(2), 166-177.

[34]. Tarrés, F., Rama, A., & Torres, L. (2005, June). A novel method for face recognition under partial occlusion or facial expression variations. In Proceedings of the 47th International Symposium (ELMAR-2005) (pp. 163-166).

[35]. Vyas, R. A., & Shah, S. M. (2017). Comparision of PCA and LDA techniques for face recognition feature based extraction with accuracy enhancement. International Research Journal of Engineering and Technology (IRJET), 4(6), 3332-3336.

[36]. Wang, J., & Yang, H. (2008, May). Face detection based on template matching and 2DPCA algorithm. In 2008 Congress on Image and Signal Processing, 4, 575-579. IEEE. https://doi.org/10.1109/CISP.2008.270

[37]. Wei, X., Li, C., & Hu, Y. (2012). Robustface recognition under varying illumination and occlusion considering structured sparsity. 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA). https://doi.org/10.1109/DICTA.2012.6411704

[38]. Wright, J., & Hua, G. (2009, June). Implicit elastic matching with random projections for pose-variant face recognition. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1502-1509). IEEE. https://doi.org/10.1109/CVPR.2009.5206786

[39]. Wu, C., Wang, S., & Ji, Q. (2015, May). Multi-instance hidden markov model for facial expression recognition. In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 1, 1-6. IEEE.

[40]. Xie, J. (2009). Face recognition based on Curvelet transform and LS-SVM. In Proceedings of the 2009 International Symposium on Information Processing (ISIP'09) (pp. 140-143). Academy Publisher.

[41]. Yi, D., Lei, Z., & Li, S. Z. (2015, May). Shared representation learning for heterogenous face recognition. In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 1, 1-7. IEEE. https://doi.org/10.1109/FG.2015.7163093

[42]. Zhang, W., Huang, D., Wang, Y., & Chen, L. (2012, December). 3D aided face recognition across pose variations. In Chinese Conference on Biometric Recognition (pp. 58-66). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_8

[43]. Zhang, X., & Gao, Y. (2009). Face recognition across pose: A review. Pattern Recognition, 42(11), 2876-2896. https://doi.org/10.1016/j.patcog.2009.04.017

[44]. Zhang, X., & Zou, J. (2008, December). Face recognition based on sub-image feature extraction and LS-SVM. In 2008 International Conference on Computer Science and Software Engineering, 1, 772-775. IEEE. https://doi.org/10.1109/CSSE.2008.1088

[45]. Zhao, W., & Chellappa, R. (2000). Illuminationsensitive face recognition using symmetric shape-from-shading. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662). https://doi.org/10.1109/CVPR.2000.855831

[46]. Zhou, Z., Wagner, A., Mobahi, H., Wright, J., & Ma, Y. (2009, September). Face recognition with contiguous occlusion using markov random fields. In 2009 IEEE 12th International Conference on Computer Vision (pp. 1050-1057). IEEE. https://doi.org/10.1109/ICCV.2009.5459383

Full Article (HTML)

Pdf

Survey Paper

A Study of the Automated Vehicle Number Plate Recognition System

Prince Rathore* , Puja Gupta, Sarthak Jain*, Yash Shrivastava****

*-**** Department of Information Technology, Shri G. S. Institute of Technology and Science, Indore, Madhya Pradesh, India.

Rathore, P., Gupta, P., Jain, S., and Shrivastava, Y. (2022). A Study of the Automated Vehicle Number Plate Recognition System. i-manager’s Journal on Pattern Recognition, 9(2), 30-36. https://doi.org/10.26634/jpr.9.2.19162

Abstract

Automatic Number Plate Recognition (ANPR) uses number plates to identify vehicles. The goal of an automated vehicle identification system is to identify the vehicle based on the number plate. The system enforces the regulations, parking, etc. It can also be used at the entrance to protect a large area, such as a military zone or the region around important government buildings like the military base, Parliament, Supreme Court, etc. The smart technology recognizes and captures the image of the vehicle. The number plate area of the vehicle is extracted using image segmentation on the image. Optical character recognition is used for character recognition. The performance data may also be compared to database records to determine the car owner, enrollment location, residence, etc. The testing showed that the improved algorithm easily recognized the number plate of a vehicle on genuine photographs.

References

[1]. Anagnostopoulos, C. N. E., Anagnostopoulos, I. E., Loumos, V., & Kayafas, E. (2006). A license platerecognition algorithm for intelligent transportation system applications. IEEE Transactions on Intelligent Transportation Systems, 7(3), 377-392. https://doi.org/10.1109/TITS.2006.880641

[2]. Bailmare, S. H., & Gadicha, A. B. (2013). A review paper on Vehicle Number Plate Recognition (VNPR) using improved character segmentation method. International Journal of Scientific and Research Publications, 3(12), 1-3.

[3]. Chen, J. J., Su, C. R., Grimson, W. E. L., Liu, J. L., & Shiue, D. H. (2011). Object segmentation of database images by dual multiscale morphological reconstructions and retrieval applications. IEEE Transactions on Image Processing, 21(2), 828-843. https://doi.org/10.1109/TIP.2011.2166558

[4]. Chen, Z. X., Liu, C. Y., Chang, F. L., & Wang, G. Y. (2009). Automatic license-plate location and recognition based on feature salience. IEEE Transactions on Vehicular Technology, 58(7), 3781-3785. https://doi.org/10.1109/TVT.2009.2013139

[5]. Deb, K., Khan, I., Saha, A., & Jo, K. H. (2012). An efficient method of vehicle license plate recognition based on sliding concentric windows and artificial neural network. Procedia Technology, 4, 812-819.

[6]. Gaikwad, D. Y., & Borole, P. B. (2014). A review paper on automatic number plate recognition (ANPR) system. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 1(1), 88-92.

[7]. Gupta, P., & Kulkarni, N. (2013). An introduction of soft computing approach over hard computing. International Journal of Latest Trends in Engineering and Technology (IJLTET), 3(1), 254-258.

[8]. Gupta, P., Shukla, M., Arya, N., Singh, U., & Mishra, K. (2022). Let the blind see: An aiiot-based device for realtime object recognition with the voice conversion. In Machine Learning for Critical Internet of Medical Things (pp. 177-198). Springer, Cham. https://doi.org/10.1007/978-3-030-80928-7_8

[9]. Hung, K. M., & Hsieh, C. T. (2010). A real-time mobile vehicle license plate detection and recognition. Journal of Applied Science and Engineering, 13(4), 433-442. https://doi.org/10.6180/jase.2010.13.4.09

[10]. Kocer, H. E., & Cevik, K. K. (2011). Artificial neural networks based vehicle license plate recognition. Procedia Computer Science, 3, 1033-1037. https://doi.org/10.1016/j.procs.2010.12.169

[11]. Kulkarni, P., Khatri, A., Banga, P., & Shah, K. (2009, April). Automatic number plate recognition (ANPR) system for Indian conditions. In 2009 19th International Conference Radioelektronika (pp. 111-114). https://doi.org/10.1109/RADIOELEK.2009.5158763

[12]. Porwal, A., Ali, A., Srivasatava, A., & Patil, M. (2020). Vehicle signal breaking alert system. International Research Journal of Engineering and Technology, 7(3), 5006-5008.

[13]. Sarfraz, M. S., Shahzad, A., Elahi, M. A., Fraz, M., Zafar, I., & Edirisinghe, E. A. (2013). Real-time automatic license plate recognition for CCTV forensic applications. Journal of Real-Time Image Processing, 8(3), 285-295. https://doi.org/10.1007/s11554-011-0232-7

[14]. Shidore, M. M., & Narote, S. P. (2011). Number plate recognition for Indian vehicles. IJCSNS International Journal of Computer Science and Network Security, 11(2), 143-146.

[15]. Singh, U., & Gupta, P. (2022). Activity detection and counting people using mask-RCNN with bidirectional ConvLSTM. Journal of Intelligent & Fuzzy Systems, 43(5), 6505-6520. https://doi.org/10.3233/JIFS-220503

[16]. Wang, S. Z., & Lee, H. J. (2007). A cascade framework for a real-time statistical plate recognition system. IEEE Transactions on Information Forensics and Security, 2(2), 267-282. https://doi.org/10.1109/TIFS.2007.897251

[17]. Wen, Y., Lu, Y., Yan, J., Zhou, Z., von Deneen, K. M., & Shi, P. (2011). An algorithm for license plate recognition applied to intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems,12(3), 830-845. https://doi.org/10.1109/TITS.2011.2114346

[18]. Zhang, Z., & Wang, C. (2012). The research of vehicle plate recognition technical based on BP neural network. AasriProcedia, 1, 74-81. https://doi.org/10.1016/j.aasri. 2012.06.013

Full Article (HTML)

Pdf

Survey Paper

A Survey of Image Encryption Algorithms for Biomedical Images

Mohan Manju* , Rajesh Kumar Pathak**

* Department of Computer Science, Shri Rawatpura Sarkar University, Raipur, Chhattisgarh, India.

** OPJS University, Rajasthan, India.

Manju, M., and Pathak, R. K. (2022). A Survey of Image Encryption Algorithms for Biomedical Images. i-manager’s Journal on Pattern Recognition, 9(2), 37-45. https://doi.org/10.26634/jpr.9.2.19091

Abstract

Biomedical imaging is a powerful tool for visualizing the body's internal organs and illnesses. Image protection is difficult in the field of biomedical imaging. Many studies have been conducted in the medical field to protect medical images. Encryption is the perfect solution for image privacy without data loss. Due to data size, redundancy, and performance limitations, traditional encryption methods cannot be directly applied to e-health medical data, especially when patient data is transferred through open channels. Thus, patients may experience a loss of confidentiality of the data content because images differ from text due to two different factors, such as loss of data and loss of privacy. Researchers have determined such protection risks and suggested several image encryption strategies for undesirable security difficulties. However, the study found that the currently offered approaches face application-specific security issues. Therefore, there is a growing demand to protect sensitive information in medical images. This paper presents a color and grayscale medical image encoding algorithm to preserve medical images. The compression and encoding performance of the proposed algorithm are analyzed and evaluated based on Mean Squared Error (MSE), Peak Signalto- Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), statistics, diversity, and entropy analysis. Matrix Laboratory (MATLAB) results show that the proposed algorithm provides high-quality reconstructed images with a good level of security during transmission.

References

[1]. Akkasaligar, P. T., & Biradar, S. (2020). Selective medical image encryption using DNA cryptography. Information Security Journal: A Global Perspective, 29(2), 91-101. https://doi.org/10.1080/19393555.2020.1718248

[2]. Alsaffar, D. M., Almutiri, A. S., Alqahtani, B., Alamri, R. M., Alqahtani, H. F., Alqahtani, N. N., & Ali, A. A. (2020, March). Image encryption based on AES and RSA algorithms. In 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-5). IEEE. https://doi.org/10.1109/ICCAIS48893.2020.9096809

[3]. Cao, C., Sun, K., & Liu, W. (2018). A novel bit-level image encr yption algorithm based on 2D-LICM hyperchaotic map. Signal Processing, 143, 122-133. https://doi.org/10.1016/j.sigpro.2017.08.020

[4]. Chai, X., Fu, X., Gan, Z., Lu, Y., & Chen, Y. (2019). A color image cryptosystem based on dynamic DNA encryption and chaos. Signal Processing, 155, 44-62. https://doi.org/10.1016/j.sigpro.2018.09.029

[5]. Chepuri, S. (2017). An RGB image encryption using RSA algorithm. International Journal of Current Trends in Engineering & Research (IJCTER), 3(3), 2455-1392.

[6]. Devi, A., Sharma, A., & Rangra, A. (2015). A review on DES, AES and blowfish for image encryption & decryption. International Journal of Computer Science and Information Technologies, 6(3), 3034-3036.

[7]. Fu, X. Q., Liu, B. C., Xie, Y. Y., Li, W., & Liu, Y. (2018). Image encr yption-then-transmission using DNA encryption algorithm and the double chaos. IEEE Photonics Journal, 10(3), 1-15. https://doi.org/10.1109/JPHOT.2018.2827165

[8]. Gamido, H. V., Sison, A. M., & Medina, R. P. (2018). Modified AES for text and image encryption. Indonesian Journal of Electrical Engineering and Computer Science, 11(3), 942-948. https://doi.org/10.11591/ijeecs.v11.i3

[9]. Gaur, P., & Kaushik, A. (2021). AES image encryption (Advanced encryption standard). International Journal for Research in Applied Science and Engineering Technology, 9(12), 1357-1363. https://doi.org/10.22214/ijraset.2021.39542

[10]. Narasingapuram, P. B., & Ponnavaikko, M. (2021). A novel attack detection and encryption framework for distributed cloud computing. Indian Journal of Computer Science and Engineering, 12(1), 210-216. https://doi.org/10.21817/indjcse/2021/ v12i1/211201249

[11]. Nematzadeh, H., Enayatifar, R., Motameni, H., Guimarães, F. G., & Coelho, V. N. (2018). Medical image encryption using a hybrid model of modified genetic algorithm and coupled map lattices. Optics and Lasers in Engineering, 110, 24-32. https://doi.org/10.1016/j.optlaseng.2018.05.009

[12]. Ning, J., Xu, J., Liang, K., Zhang, F., & Chang, E. C. (2019). Passive attacks against searchable encryption. IEEE Transactions on Information Forensics and Security, 14(3), 789-802. https://doi.org/10.1109/TIFS.2018.2866321

[13]. Parida, P., Pradhan, C., Gao, X. Z., Roy, D. S., & Barik, R. K. (2021). Image encryption and authentication with elliptic curve cryptography and multidimensional chaotic maps. IEEE Access, 9, 76191-76204. https://doi.org/10.1109/ACCESS.2021.3072075

[14]. Zhang, Q., Guo, L., & Wei, X. (2010). Image encryption using DNA addition combining with chaotic maps. Mathematical and Computer Modelling, 52(11- 12), 2028-2035. https://doi.org/10.1016/j.mcm.2010.06.005

[15]. Zhang, Y. (2018). Test and verification of AES used for image encryption. 3D Research, 9(1), 1-27. https://doi.org/10.1007/s13319-017-0154-7

[16]. Zhang, Y. Q., Hao, J. L., & Wang, X. Y. (2020). An efficient image encryption scheme based on S-boxes and fractional-order differential logistic map. IEEE Access, 8, 54175-54188. https://doi.org/10.1109/ACCESS.2020.2979827

[17]. Zhou, L., Xiao, Y., & Chen, W. (2019). Machinelearning attacks on inter ference-based optical encryption: experimental demonstration. Optics Express, 27(18), 26143-26154. https://doi.org/10.1364/OE.27.026143

[18]. Zhu, K., Lin, Z., & Ding, Y. (2019). A new RSA image encr yption algorithm based on singular value decomposition. International Journal of Pattern Recognition and Artificial Intelligence, 33(1), 1954002. https://doi.org/10.1142/S0218001419540028

i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 11 Issue 1

Most Read

Most Cited

Volume 9 Issue 2 July - December 2022

A Model-Free Gait Recognition Approach for Person Identification with Covariates

Surekha Samsani *

Department of Computer Science and Engineering, University College of Engineering Kakinada (A), JNTUK, Andhra Pradesh, India.

Samsani, S. (2022). A Model-Free Gait Recognition Approach for Person Identification with Covariates. i-manager’s Journal on Pattern Recognition, 9(2), 1-8. https://doi.org/10.26634/jpr.9.2.19124

Abstract

References

Full Article (HTML)

Pdf

Weed Identification using Machine Learning Models

Vandana Saini* , Shalini**, Krishan Kumar Sharma***

*-*** Department of Computer Science & Informatics, University of Kota, Kota, Rajasthan, India.

Saini, V., Shalini, and Sharma, K. K. (2022). Weed Identification using Machine Learning Models. i-manager’s Journal on Pattern Recognition, 9(2), 9-16. https://doi.org/10.26634/jpr.9.2.19086

Abstract

References

Full Article (HTML)

Pdf

Review on Various Face Recognition Databases

Khushi Bhoj* , Kuldeep Choksi**, Rishi Kitawat***, Manish Rana****

*-**** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, India.

Bhoj, K., Choksi, K., Kitawat, R., and Rana, M. (2022). Review on Various Face Recognition Databases. i-manager’s Journal on Pattern Recognition, 9(2), 17-29. https://doi.org/10.26634/jpr.9.2.19189

Abstract

References

Full Article (HTML)

Pdf

A Study of the Automated Vehicle Number Plate Recognition System

Prince Rathore* , Puja Gupta**, Sarthak Jain***, Yash Shrivastava****

*-**** Department of Information Technology, Shri G. S. Institute of Technology and Science, Indore, Madhya Pradesh, India.

Rathore, P., Gupta, P., Jain, S., and Shrivastava, Y. (2022). A Study of the Automated Vehicle Number Plate Recognition System. i-manager’s Journal on Pattern Recognition, 9(2), 30-36. https://doi.org/10.26634/jpr.9.2.19162

Abstract

References

Full Article (HTML)

Pdf

A Survey of Image Encryption Algorithms for Biomedical Images

Mohan Manju* , Rajesh Kumar Pathak**

* Department of Computer Science, Shri Rawatpura Sarkar University, Raipur, Chhattisgarh, India. ** OPJS University, Rajasthan, India.

Manju, M., and Pathak, R. K. (2022). A Survey of Image Encryption Algorithms for Biomedical Images. i-manager’s Journal on Pattern Recognition, 9(2), 37-45. https://doi.org/10.26634/jpr.9.2.19091

Abstract

References

Full Article (HTML)

Pdf

Vandana Saini* , Shalini, Krishan Kumar Sharma*

Khushi Bhoj* , Kuldeep Choksi, Rishi Kitawat*, Manish Rana****

Prince Rathore* , Puja Gupta, Sarthak Jain*, Yash Shrivastava****

* Department of Computer Science, Shri Rawatpura Sarkar University, Raipur, Chhattisgarh, India.

** OPJS University, Rajasthan, India.