AI-BASED HUMAN FACIAL RECOGNITION USING DEEP LEARNING WITH YALE FACE DATASET

Tripathi Kinnari  Purshottam; Ramachandran P

doi:10.30572/2018/KJE/170311

Authors

Tripathi Kinnari Purshottam Computer science and Engineering Department, FITCS, Parul University, Vadodara, Gujarat, India
Ramachandran P Parul Institute of Engineering and Technology-MCA, FITCS, Parul University, Vadodara, Gujarat, India

DOI:

https://doi.org/10.30572/2018/KJE/170311

Keywords:

HFRs, face analysis, face database, Deep Learning, yale faces, facial detections

Abstract

Human Faces as a biometric feature are applied in multiple applications where both public and private organizations have preserved facial images for membership cards, passports, or personal identifications. They have been used as referential databases in investigations and match facial images of victims, witnesses, or offenders. Moreover, the widespread usages of smartphones and digital cameras have made it simple to share created facial images using social networks. Thus, Human Facial Recognitions (HFRs) have been an exciting and quickly expanding field of study. Real time applications include HFRs for identifications, access controls, forensics, and human-computer interactions. Though studies have been proposed for HFRs, there are areas that are wanting in implementations. Hence, this work attempts to fill up these gaps in HFRs with its suggested AI Based Recognitions of Faces (AIBRF). The schema uses Deep Learning (DL) techniques for identifying faces. The schema is trained using Yale Face dataset and achieves 99% accuracy in HRFs

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References

Adjabi, I., Ouahabi, A., Benzaoui, A. and Taleb-Ahmed, A. (2020) 'Past, present, and future of HFRs: A review', Electronics, 9, p. 1188.

Alameda-Pineda, X., Ricci, E. and Sebe, N. (2016) 'Analyzing the performance of CNN-based HFRs for occluded face verification', in Proceedings of the 2016 IEEE International Joint Conference on Biometrics (IJCB), Ljubljana, Slovenia, 25–28 September, pp. 1–8.

Alsmadi, M. (2016) 'HFRs under expression variations', International Arab Journal of Information Technology, 13, pp. 133–141.

Audette, P.-L., Côté, L., Blais, C., Duncan, J., Gingras, F. and Fiset, D. (2025) 'Part-based processing, but not holistic processing, predicts individual differences in HFRs abilities', Cognition, 256, p. 106057.

Bao, X., Hu, Y., Chen, Y. and Sun, L. (2018) 'Deep learning-based HFRs: A survey', Journal of Sensors, 2018.

Benzaoui, A., Bourouba, H. and Boukrouche, A. (2012) 'System for automatic faces detection', in Proceedings of the 2012 3rd International Conference on Image Processing, Theory, Tools and Applications (IPTA), Istanbul, Turkey, 15–18 October, pp. 354–358.

Bledsoe, W.W. (1964) The Model Method in Facial Recognition, Technical Report, Panoramic Research, Inc., Palo Alto, CA, USA.

Cavazos, J.G., Phillips, P.J., Castillo, C.D. and O’Toole, A.J. (2020) 'Accuracy comparison across HFRs algorithms: Where are we on measuring race bias?', IEEE Transactions on Biometrics, Behavior, and Identity Science, 3, pp. 101–111.

Chai, X. and Wu, Y. (2017) 'HFRs based on deep learning: A survey', Neurocomputing, 235, pp. 166–177.

Chen, J., Chen, Y., Liu, X. and Tang, Y. (2019) 'A novel deep learning based HFRs algorithm', in Proceedings of the 2019 13th IEEE International Conference on ASIC (ASICON), Chongqing, China, 29 October–1 November, pp. 1304–1307.

Chiachia, G., Sgouropoulos, D. and Pitas, I. (2018) 'Deep learning for HFRs: A comprehensive review', in Handbook of Neural Computation, Springer, Berlin/Heidelberg, Germany, pp. 1–36.

Collobert, R., Weston, J., Karlen, M., Kavukcuoglu, K. and Kuksa, P. (2011) 'Natural language processing (almost) from scratch', Journal of Machine Learning Research, 12(1), pp. 2493–2537.

da Silva Vieira, G., Rocha, B.M., Fonseca, A.U., de Sousa, N.M., Ferreira, J.C., Cabacinha, C.D. and Soares, F. (2022) 'Automatic detection of insect predation through the segmentation of damaged leaves', Smart Agricultural Technology, 2, p. 100056.

Dantcheva, A., Chen, C. and Ross, A. (2012) 'Can facial cosmetics affect the matching accuracy of face recognition systems?', in Proceedings of the 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), Arlington, VA, USA, 23–27 September, pp. 391–398.

De Carrera, P.F. and Marques, I. (2010) 'HFRs Algorithms', Master’s Thesis, Universidad Euskal Herriko, Leioa, Spain.

Ding, C., Xu, C. and Tao, D. (2017) 'Multi-view deep learning for consistent facial expression recognition', IEEE Transactions on Affective Computing, 9, pp. 578–584.

Faruqe, O. and Hasan, M. (2009) 'HFRs using PCA and SVM', in Proceedings of the 2009 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication, Hong Kong, China, 20–22 August, pp. 97–101.

Fazilova, S., Mirzaeva, O., Radjabov, S., Mirzaeva, G. and Rabbimov, I. (2024) 'Construction of a recognition algorithm based on the assessment of the interdependence between local elements of the face image', Procedia Computer Science, 234, pp. 131–139.

Graves, A. and Schmidhuber, J. (2010) 'Framewise phoneme classification with bidirectional LSTM and other neural network architectures', Neural Networks, 18(5–6), p. 602.

Gross, R., Matthews, I. and Baker, S. (2017) 'Eigen light-fields and HFRs across pose', in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, USA, 30 May–3 June, pp. 1–7.

Guo, G. and Zhang, N. (2019) 'A survey on deep learning based face recognition', Computer Vision and Image Understanding, 189, p. 10285.

Guo, Y., Zhang, L., Hu, Y., He, X. and Gao, J. (2016) 'Ms-celeb-1m: A dataset and benchmark for large-scale HFRs', in European Conference on Computer Vision, Springer, Berlin/Heidelberg, Germany, pp. 87–102.

Han, H., Jain, A.K. and Learned-Miller, E.G. (2017) 'Matching-aware image-to-set HFRs', IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, pp. 1790–1802.

Huang, G.B., Mattar, M.A., Berg, T.L. and Learned-Miller, E. (2008) 'Labeled Faces in the Wild: A database for studying HFRs in unconstrained environments', in Proceedings of the Workshop on Faces published in “Dans Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition”, Marseille, France, 16 October, pp. 1–6.

Huang, P., Hong, X., Chen, Z. and Shang, Z. (2019) 'Facial expression recognition with convolutional neural networks: A comparative study', Neural Computing and Applications, 31, pp. 8881–8889.

Kalayeh, M.M., Basaru, R.R. and Murthy, O.V.R. (2019) 'A survey on deep learning techniques for HFRs', Journal of Ambient Intelligence and Humanized Computing, 10, pp. 3817–3839.

Kaur, N. and Singh, H. (2019) 'Automated facial expression recognition using deep learning: A review', IET Image Processing, 13, pp. 965–978.

Kaur, P., Krishan, K., Sharma, S.K. and Kanchan, T. (2020) 'Facial-recognition algorithms: A literature review', Medical Science and Law, 60, pp. 131–139.

Khan, S.H., Hayat, M., Bennamoun, M., Sohel, F. and Togneri, R. (2017) 'Cost sensitive learning of deep feature representations from imbalanced data', IEEE Transactions on Neural Networks and Learning Systems, 28, pp. 2388–2399.

Kortli, Y., Jridi, M., Al Falou, A. and Atri, M. (2020) 'A review of face recognition methods', Sensors, 20, p. 342.

Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) 'Imagenet classification with deep convolutional neural networks', in International Conference on Neural Information Processing Systems, pp. 1097–1105.

Li, H., Chen, Y., Liu, X. and Gao, Y. (2020) 'A multi-scale hierarchical deep neural network for facial expression recognition', Knowledge-Based Systems, 201, p. 105926.

Li, S., Wang, Z., Tan, X. and Huang, K. (2018) 'HFRs based on multi-feature fusion using deep convolutional neural network', Journal of Visual Communication and Image Representation, 57, pp. 366–376.

Li, S.Z. and Jain, A.K. (2011) Handbook of Face Recognition, 2nd ed., Springer Publishing Company, New York, NY, USA.

Mathias, M., Benenson, R., Pedersoli, M. and Van Gool, L. (2014) 'Face detection without bells and whistles', in European Conference on Computer Vision, pp. 720–735. Springer.

Morder-Intelligence (2020) 'Facial recognition market', Mordor Intelligence. Available at: https://www.mordorintelligence.com/industry-reports/facial-recognition-market (Accessed: 21 July 2020).

O’Toole, A.J., Roark, D.A. and Abdi, H. (2002) 'Recognizing moving faces: A psychological and neural synthesis', Trends in Cognitive Sciences, 6, pp. 261–266.

Ouamane, A., Benakcha, A., Belahcene, M. and Taleb-Ahmed, A. (2015) 'Multimodal depth and intensity face verification approach using LBP, SLF, BSIF, and LPQ local features fusion', Pattern Recognition and Image Analysis, 25, pp. 603–620.

Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J. and Worek, W. (2005) 'Overview of the face recognition grand challenge', in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, 20–26 June, pp. 947–954.

Phillips, P.J., Wechsler, H., Huang, J. and Rauss, P. (1998) 'The FERET database and evaluation procedure for face recognition algorithms', Image and Vision Computing, 16, pp. 295–306.

Porter, G. and Doran, G. (2000) 'An anatomical and photographic technique for forensic facial identification', Forensic Science International, 114, pp. 97–105.

Reddy, G.R., Mohanty, S.P. and Sahoo, S.K. (2019) 'A review on applications of deep learning techniques in HFRs', International Journal of Advanced Science and Technology, 28, pp. 248–257.

Rowley, H., Baluja, S. and Kanade, T. (1998) 'Neural network-based face detection', IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, pp. 23–38.

Seetharam, M.R. and Mohanty, S.P. (2019) 'An efficient HFRs using machine learning algorithms', International Journal of Engineering and Advanced Technology, 9, pp. 13–16.

Sinha, P., Balas, B., Ostrovsky, Y. and Russell, R. (2006) 'Face recognition by humans: Nineteen results all computer vision researchers should know about', Proceedings of the IEEE, 94, pp. 1948–1962.

Smith, J. and Doe, A. (2020) Introduction to Support Vector Machines: Theory and Applications, Academic Press, Cambridge, MA, USA.

Taigman, Y., Yang, M., Ranzato, M. and Wolf, L. (2014) 'Deepface: Closing the gap to human-level performance in face verification', in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June, pp. 1701–1708.

Tang, F., Lim, S.H., Chang, N.L. and Tao, H. (2009) 'A novel feature descriptor invariant to complex brightness changes', in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June, pp. 2631–2638.

Turk, M. and Pentland, A. (1991) 'Eigenfaces for recognition', Journal of Cognitive Neuroscience, 3, pp. 71–86.

Viola, P. and Jones, M.J. (2004) 'Robust real-time face detection', International Journal of Computer Vision, 57(2), pp. 137–154.

Wang, D., Gong, D., Zhu, X. and Zhou, W. (2020) 'A survey on deep learning for facial analysis', Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10, p. e1352.

Wayman, J., Jain, A., Maltoni, D. and Maio, D. (2005) Biometric Recognition: Principles and Practice, Springer, Berlin/Heidelberg, Germany.

Wu, Y., Liu, Q., Yang, Y. and Pan, J. (2020) 'HFRs with noisy images by a novel deep learning model', Journal of Ambient Intelligence and Humanized Computing, 11, pp. 2037–2047.