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

Current Issue Vol. 11 Issue 2

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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

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[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

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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

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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.

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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.

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[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.

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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

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[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.

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[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 2

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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

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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

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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

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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

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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

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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.