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

Current Issue Vol. 11 Issue 1

Most Cited

Volume 7 Issue 2 July - December 2020

Research Paper

Two-Pass Segmentation for Marathi Character Extraction

Madhav A. Kankhar* , C. Namrata Mahender **

*-** Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India.

Kankhar, M. A., and Mahender, C. N. (2020). Two-Pass Segmentation for Marathi Character Extraction. i-manager's Journal on Pattern Recognition, 7(2), 1-6. https://doi.org/10.26634/jpr.7.2.18148

Abstract

Handwriting recognition is very complex but a much needed system for various purposes. Work on English language can be seen in full swing as compared to Indian languages. Free style handwriting has various challenges due to the individual variation in writing style, along with half characters, joint characters, overlapped characters, etc. are major when considering written in Devanagari script. Computational studies of Indian languages written using Devanagari script, including Marathi, are in its primary stage. The present work focuses on proper character segmentation in Marathi words for better post-processing and recognition rate as segmentation is an important stage that affects post-processing performance. Data is collected from native speakers of the Marathi language, with 3000 samples handwritten words in Marathi language for numbers from one to ten, and this paper discusses the proposed two-pass segmentation approach to extract written Marathi script.

References

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[5]. Golait, S. S., Malik, L. G., & Thomas, A. (2017a). Feature extraction for Marathi compound character using edge map. International Journal of Scientific & Engineering Research, 8(2), 1095-1101.

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[10]. Khobragade, R. N., Koli, N. A., & Lanjewar, V. T. (2020). Challenges in recognition of online and off-line compound handwritten characters: A review. Smart Trends in Computing and Communications, 375-383. https://doi. org/10.1007/978-981-15-0077-0_38

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Research Paper

Analysing Meticulous Behavior of Learners from Non-Verbal Cues

Mageshwaran L.* , Keerthivasan S. E. , Hariharan K.*, Rajasekaran T.****

*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India.

Mageshwaran, L., Keerthivasan, S. E., Hariharan, K. and Rajasekaran, T. (2020). Analysing Meticulous Behavior of Learners from Non-Verbal Cues. i-manager's Journal on Pattern Recognition, 7(2), 7-13. https://doi.org/10.26634/jpr.7.2.18129

Abstract

Face emotion recognition is the most important part in deep learning. It is the best way to communicate in a non-verbal manner. The main issue, which has existed since the time, is that teachers are unable to determine whether or not their students have grasped the subject in class. The proposed model in this paper will address this issue. Face emotion recognition is becoming more popular with Artificial Intelligence. In our model, Convolutional Neural Networks is used for the emotion recognition. This is divided into two parts; face detection and extraction can be done using Haar cascades and the emotion will be extracted from the face using CNN on FER 2013. Then the emotion is collected continuously and the average will be stored in MySQL database. A user interface will be created to teachers and admin so that they can view the data of students at any time and also generate report on it. This proposed model will give the accuracy of 90%.

References

[1]. Ashwin, T. S., & Guddeti, R. M. R. (2019). Unobtrusive behavioral analysis of students in classroom environment using non-verbal cues. IEEE Access, 7, 150693-150709. https://doi.org/10.1109/ACCESS.2019.2947519

[2]. El Hammoumi, O., Benmarrakchi, F., Ouherrou, N., El Kafi, J., & El Hore, A. (2018, May). Emotion recognition in e- learning systems. In 2018, 6th International Conference on Multimedia Computing and Systems (ICMCS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICMCS.2018.8525872

[3]. Jaiswal, A., Raju, A. K., & Deb, S. (2020, June). Facial emotion detection using deep learning. In 2020, International Conference for Emerging Technology (INCET) (pp. 1-5). IEEE. https://doi.org/10.1109/INCET49848.2020. 9154121

[4]. Krithika, L. B. (2016). Student Emotion Recognition System (SERS) for e-learning improvement based on learner concentration metric. Procedia Computer Science, 85, 767-776. https://doi.org/10.1016/j.procs.2016.05.264

[5]. Lasri, I., Solh, A. R., & El Belkacemi, M. (2019, October). Facial emotion recognition of students using convolutional neural network. In 2019, 3rd International Conference on Intelligent Computing in Data Sciences (ICDS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICDS47004.2019.8942386

[6]. Mavani, V., Raman, S., & Miyapuram, K. P. (2017). Facial expression recognition using visual saliency and deep learning. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 2783- 2788). https://doi.org/10.1109/ICCVW.2017.327

[7]. Monkaresi, H., Bosch, N., Calvo, R. A., & D'Mello, S. K. (2016). Automated detection of engagement using videobased estimation of facial expressions and heart rate. IEEE Transactions on Affective Computing, 8(1), 15-28. https://doi.org/10.1109/TAFFC.2016.2515084

[8]. Nerkar, M. P., Sawant, A., Jawade, S., Shinde, R., & Thakur, R. (2020). Automatic recognition of student engagement using deep learning and facial expression. International Engineering Research Journal. (Special Issue), 398-401. Retrieved from http://www.ierjournal.org/ pupload /NCIET-2020/398-401.pdf

[9]. Putra, W. B., & Arifin, F. (2019, November). Real-time emotion recognition system to monitor student's mood in a classroom. In Journal of Physics: Conference Series (Vol. 1413, No. 1, p. 012021). International Conference on Electrical, Electronic, Informatic and Vocational Education. IOP Publishing. 10.1088/1742-6596/1413/1/012021

[10]. Rzayeva, Z., & Alasgarov, E. (2019). Facial emotion th recognition using convolutional neural networks. In IEEE 13 International Conference on Application of Information and Communication Technologies (AICT). https://doi.org/ 10.1109/AICT47866.2019.8981757

[11]. Sharma, A., & Mansotra, V. (2019). Deep learning based student emotion recognition from facial expressions in classrooms. International Journal of Engineering and Advanced Technology (IJEAT), 8(6), 4691-4699. https://doi. org/10.35940/ijeat.F9170.088619

[12]. Singh, S., & Nasoz, F. (2010). Facial expression th recognition with convolutional neural networks. In 10 Annual Computing and Communication Workshop and Conference (CCWC). https://doi.org/10.1109/CCWC4752 4.2020.9031283

[13]. Wang, W., Xu, K., Niu, H., & Miao, X. (2020). Emotion recognition of students based on facial expressions in online education based on the perspective of computer simulation. Complexity, 2020: 4065207. https://doi.org/ 10.1155/2020/4065207

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Research Paper

Robust Facial Expression Recognition based on Convolutional Neural Network in Pose and Occlusion

Shalmiya P.* , G. Thirugnanam **

*-** Department of Electronics and Communication Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India.

Shalmiya, P., and Thirugnanam, G. (2020). Robust Facial Expression Recognition based on Convolutional Neural Network in Pose and Occlusion. i-manager's Journal on Pattern Recognition, 7(2), 14-22. https://doi.org/10.26634/jpr.7.2.18083

Abstract

Facial expression plays an important role in human-human communication. Facial Expression Recognition (FER) has various applications in attendance management system, service robots, intelligent tutoring system, driver fatigue monitoring, human behavior understanding, detection of mental disorders and has various medical and surveillance applications. The various limitations of FER are occlusion, illumination variation, variant pose, poor image quality, etc. This work mainly focuses on occluded and pose variations datasets. These issues have included wide interest in FER where it leads to unseen regions of input faces which brings difficulty for face identification and face recognition and also affects the feature extraction and classification process. Thus, these effects can be reduced and the expressions are recognised using Convolutional Neural Network (CNN) architecture. Finally, the performance metrics of the model has been measured with accuracy values of the system.

References

[1]. Alizadeh, S., & Fazel, A. (2017). Convolutional neural networks for facial expression recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Retrieved from http://cs231n.stanford.edu/reports/ 2016/pdfs/005_Report.pdf

[2]. Cotter, S. F. (2010a, August). Weighted voting of sparse representation classifiers for facial expression recognition. th In 2010, 18 European Signal Processing Conference (pp. 1164-1168). IEEE.

[3]. Cotter, S. F. (2010b, March). Sparse representation for accurate classification of corrupted and occluded facial expressions. In 2010, IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 838-841). IEEE. https://doi.org/10.1109/ICASSP.2010.5494903

[4]. Ebrahimi Kahou, S., Michalski, V., Konda, K., Memisevic, R., & Pal, C. (2015, November). Recurrent neural networks for emotion recognition in video. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (pp. 467-474). https://doi.org/10.1145/2818346.2830596

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[8]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.

[9]. Li, Y., Zeng, J., Shan, S., & Chen, X. (2018). Occlusion aware facial expression recognition using CNN with attention mechanism. IEEE Transactions on Image Processing, 28(5), 2439-2450. https://doi.org/10.1109/TIP. 2018.2886767

[10]. Liu, P., Han, S., Meng, Z., & Tong, Y. (2014). Facial expression recognition via a boosted deep belief network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1805-1812).

[11]. Mollahosseini, A., Chan, D., & Mahoor, M. H. (2016, March). Going deeper in facial expression recognition using deep neural networks. In 2016, IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1-10). IEEE. https://doi.org/10.1109/WACV.2016.7477 450

[12]. Pantic, M., & Rothkrantz, L. J. (2004). Facial action recognition for facial expression analysis from static face images. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(3), 1449-1461. https://doi.org/10.1109/TSMCB.2004.825931

[13]. Singh, S., & Nasoz, F. (2020). Facial expression th recognition with convolutional neural networks. In 2020, 10 Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0324-0328). IEEE. https://doi. org/10.1109/CCWC47524.2020.9031283

[14]. Wang, K., Peng, X., Yang, J., Meng, D., & Qiao, Y. (2020). Region attention networks for pose and occlusion robust facial expression recognition. IEEE Transactions on Image Processing, 29, 4057-4069. https://doi.org/10.110 9/TIP.2019.2956143

[15]. Wu, Y., & Qiu, W. (2017, July). Facial expression recognition based on improved local ternary pattern and stacked auto-encoder. In AIP Conference Proceedings (Vol. 1864, No. 1). AIP Publishing LLC. https://doi.org/10.106 3/1.4992948

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Research Paper

Noise Robust Speech Recognition under Noisy Environments

P. Sunitha* , V. Sailaja , B. Vasantha Lakshmi *

*-*** Department of Electronics and Communications Engineering, Pragati Engineering College, Andhra Pradesh, India.

Sunitha, P., Sailaja, V., and Lakshmi, B. V. (2020). Noise Robust Speech Recognition under Noisy Environments. i-manager's Journal on Pattern Recognition, 7(2), 23-28. https://doi.org/10.26634/jpr.7.2.18094

Abstract

This paper presents a new method for improving the recognition accuracy of a speech recognition system in a noisy environment by using robust speech enhancement technique with the aid of noise estimation algorithm. The robustness of a speech recognition system can be improved by improving the speech quality at signal level by means of noise suppression algorithms, feature extraction level or at modelling phase. The proposed method uses robust speech enhancement technique as a pre-processing operation to improve the recognition accuracy in presence of noise. The suggested method is evaluated in terms of recognition accuracy. The suggested method yields better results in terms of recognition accuracy in presence of eight different types of non-stationary noises under different SNR levels when compared with the baseline speech recognition system.

References

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[3]. Gupta, V. K., Bhowmick, A., Chandra, M., & Sharan, S. N. (2011, February). Speech enhancement using MMSE estimation and spectral subtraction methods. In 2011, International Conference on Devices and Communications (ICDeCom) (pp. 1-5). IEEE. https://doi.org/10.1109/ICDE COM.2011.5738532

[4]. Hidayat, R., Bejo, A., Sumaryono, S., & Winursito, A. (2018, July). Denoising speech for MFCC feature extraction using wavelet transformation in speech recognition system. In 2018, 10th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 280-284). IEEE. https://doi.org/10.1109/ICITEED.2018. 8534807

[5]. Hirsch, H. G., & Ehrlicher, C. (1995, May). Noise estimation techniques for robust speech recognition. In 1995, International Conference on Acoustics, Speech, and Signal Processing (Vol. 1, pp. 153-156). IEEE. https://doi.org/ 10.1109/ICASSP.1995.479387

[6]. Karray, L., & Martin, A. (2003). Towards improving speech detection robustness for speech recognition in adverse conditions. Speech Communication, 40(3), 261- 276. https://doi.org/10.1016/S0167-6393(02)00066-3

[7]. Kinnunen, T., Saeidi, R., Sedlák, F., Lee, K. A., Sandberg, J., Hansson-Sandsten, M., & Li, H. (2012). Low-variance multitaper MFCC features: A case study in robust speaker verification. IEEE Transactions on Audio, Speech, and Language Processing, 20(7), 1990-2001. https://doi.org/ 10.1109/TASL.2012.2191960

[8]. Lockwood, P., Boudy, J., & Blanchet, M. (1992, March). Non-linear spectral subtraction (NSS) and hidden Markov models for robust speech recognition in car noise environments. In Acoustics, Speech, and Signal Processing, IEEE International Conference (Vol. 1, pp. 265- 268). IEEE Computer Society. https://doi.ieeecomputer society.org/10.1109/ICASSP.1992.225921

[9]. Martin, R. (1994). Spectral subtraction based on minimum statistics. In Proceedings of European Signal Processing (pp.1182-1185).

[10]. Martin, R. (2001). Noise power spectral density estimation based on optimal smoothing and minimum statistics. IEEE Transactions on Speech and Audio Processing, 9(5), 504-512. https://doi.org/10.1109/89.928915

[11]. Ris, C., & Dupont, S. (2001). Assessing local noise level estimation methods: Applications to noise robust ASR. Speech Communication, 34(1-2), 141-158. https://doi. org/10.1016/S0167-6393(00)00051-0

[12]. Shao, Y., & Chang, C. H. (2010). Bayesian separation with sparsity promotion in perceptual wavelet domain for speech enhancement and hybrid speech recognition. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(2), 284-293. https://doi.org/ 10.1109/TSMCA.2010.2069094

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Research Paper

CV-SAS: Computer Vision Modeled Seamless Stress Analysis System for Workers

B. Muthu Senthil * , Dinesh E. , Dinesh T. *, Goutham Raj S. ****

*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India.

Muthusenthil, B., Dinesh, E., Dinesh, T., and Raj, S. G. (2020). CV-SAS: Computer Vision Modeled Seamless Stress Analysis System for Workers. i-manager's Journal on Pattern Recognition, 7(2), 29-36. https://doi.org/10.26634/jpr.7.2.18127

Abstract

People spend most of their time in the workplace, often with high workloads and time pressure, a practice that contributes to increased stress levels. An accurate stress assessment method may thus be of importance to clinical intervention and diseases prevention. While different neuro imaging modalities have been proposed to detect mental stress, each modality experiences certain limitations. This paper proposes the ability of a computer to detect and analyze the stress in human beings with greater accuracy. The system uses the webcam to detect the facial features and perform a mood analysis, which is eventually used for detecting stress. Then, the features are fed into the SVM model as an input and using the machine learning approach the stress is classified.

References

[1]. Arpaia, P., Moccaldi, N., Prevete, R., Sannino, I., & Tedesco, A. (2020). A wearable EEG instrument for realtime frontal asymmetry monitoring in worker stress analysis. IEEE Transactions on Instrumentation and Measurement, 69(10), 8335-8343.

[2]. Bobade, P., & Vani, M. (2020, July). Stress detection with machine learning and deep learning using multimodal physiological data. In 2020, Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 51-57). IEEE.

[3]. Giannakakis, G., Pediaditis, M., Manousos, D., Kazantzaki, E., Chiarugi, F., Simos, P. G., ..., & Tsiknakis, M. (2017). Stress and anxiety detection using facial cues from videos. Biomedical Signal Processing and Control, 31, 89- 101.

[4]. Global Organization for Stress on Stress Facts. (2020). Retrieved from http://www.gostress.com/stress-facts

[5]. Kim, J., & André, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2067-2083.

[6]. Koldijk, S., Neerincx, M. A., & Kraaij, W. (2016). Detecting work stress in offices by combining unobtrusive sensors. IEEE Transactions on Affective Computing, 9(2), 227-239.

[7]. Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M. A., & Kraaij, W. (2014, November). The swell knowledge work dataset for stress and user modeling research. In th Proceedings of the 16 International Conference on Multimodal Interaction (pp. 291-298). https://doi.org/10.1 145/2663204.2663257

[8]. Minguillon, J., Perez, E., Lopez-Gordo, M. A., Pelayo, F., & Sanchez-Carrion, M. J. (2018). Portable system for realtime detection of stress level. Sensors, 18(8), 2504. https://doi.org/10.3390/s18082504

[9]. Padmaja, B., Prasad, V. R., & Sunitha, K. V. N. (2018). A machine learning approach for stress detection using a wireless physical activity tracker. International Journal of Machine Learning and Computing, 8(1), 33-38. https://doi. org/10.18178/ijmlc.2018.8.1.659

[10]. Sarkar, B. (2020, December 7). Seven in 10 Indian workers say they're experiencing stress at work on weekly basis: Report. The Economic Times. Retrieved from https://economictimes.indiatimes.com/news/company/c orporate-trends/seven-in-10-indian-workers-say-theyreexperiencing- stress-at-work-on-weekly-basis-report/articles how/79607886.cms?from=mdr

[11]. Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., & Van Laerhoven, K. (2018, October). Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (pp. 400-408). https://doi.org/10.1145/3242969.3242985

[12]. Spiers, D. L. (2016). Facial emotion detection using deep learning. (Degree project). Department of Information Technology, Uppsala University, Sweden.

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

Current Issue Vol. 11 Issue 1

Most Read

Most Cited

Volume 7 Issue 2 July - December 2020

Two-Pass Segmentation for Marathi Character Extraction

Madhav A. Kankhar* , C. Namrata Mahender **

*-** Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India.

Kankhar, M. A., and Mahender, C. N. (2020). Two-Pass Segmentation for Marathi Character Extraction. i-manager's Journal on Pattern Recognition, 7(2), 1-6. https://doi.org/10.26634/jpr.7.2.18148

Abstract

References

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Analysing Meticulous Behavior of Learners from Non-Verbal Cues

Mageshwaran L.* , Keerthivasan S. E. **, Hariharan K.***, Rajasekaran T.****

*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India.

Mageshwaran, L., Keerthivasan, S. E., Hariharan, K. and Rajasekaran, T. (2020). Analysing Meticulous Behavior of Learners from Non-Verbal Cues. i-manager's Journal on Pattern Recognition, 7(2), 7-13. https://doi.org/10.26634/jpr.7.2.18129

Abstract

References

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Robust Facial Expression Recognition based on Convolutional Neural Network in Pose and Occlusion

Shalmiya P.* , G. Thirugnanam **

*-** Department of Electronics and Communication Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India.

Shalmiya, P., and Thirugnanam, G. (2020). Robust Facial Expression Recognition based on Convolutional Neural Network in Pose and Occlusion. i-manager's Journal on Pattern Recognition, 7(2), 14-22. https://doi.org/10.26634/jpr.7.2.18083

Abstract

References

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Noise Robust Speech Recognition under Noisy Environments

P. Sunitha* , V. Sailaja **, B. Vasantha Lakshmi ***

*-*** Department of Electronics and Communications Engineering, Pragati Engineering College, Andhra Pradesh, India.

Sunitha, P., Sailaja, V., and Lakshmi, B. V. (2020). Noise Robust Speech Recognition under Noisy Environments. i-manager's Journal on Pattern Recognition, 7(2), 23-28. https://doi.org/10.26634/jpr.7.2.18094

Abstract

References

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CV-SAS: Computer Vision Modeled Seamless Stress Analysis System for Workers

B. Muthu Senthil * , Dinesh E. **, Dinesh T. ***, Goutham Raj S. ****

*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India.

Muthusenthil, B., Dinesh, E., Dinesh, T., and Raj, S. G. (2020). CV-SAS: Computer Vision Modeled Seamless Stress Analysis System for Workers. i-manager's Journal on Pattern Recognition, 7(2), 29-36. https://doi.org/10.26634/jpr.7.2.18127

Abstract

References

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Mageshwaran L.* , Keerthivasan S. E. , Hariharan K.*, Rajasekaran T.****

P. Sunitha* , V. Sailaja , B. Vasantha Lakshmi *

B. Muthu Senthil * , Dinesh E. , Dinesh T. *, Goutham Raj S. ****