Abstract
Recognizing faces from the video employs artificial intelligence-based computer technology. This includes applications such as law enforcement, biometrics, security, personal safety, etc., for tracking and enabling surveillance in real-time. However, detection and recognition of the face from the video are influenced by the change in variation of pose, brightness, occlusion, expression, and resolutions. While facial images are simple to detect, others may necessitate the use of specialized software. To address those challenges we propose an efficient face detection and recognition system with optimal features. Initially, the keyframes are extracted by the Key Frame Extraction method which utilizes Wavelet Information. Subsequently, the characteristics such as holo-entropy, appearance features, SURF feature, and multi-angle movement feature are extracted. The Oppositional Grass Hopper Optimization Algorithm is used to identify optimal features from this large feature set. The extracted features are classified using the fuzzy classifier that was designed. The proposed method's performance is validated using several benchmark video datasets and its efficiency is measured in terms of keyframe extraction time, sensitivity, accuracy, and specificity by comparing it to other state-of-the-art approaches. The proposed method outperforms previous state-of-the-art methods in terms of recognition accuracy.
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References
Al-Obaydy, W. N., & Suandi, S. A. (2020). Automatic pose normalization for open-set single-sample face recognition in video surveillance. Multimedia Tools and Applications, 79(3), 2897–2915.
Lei, Z., Zhang, X., Yang, S., Ren, Z., & Akindipe, O. F. (2020). RFR-DLVT: A hybrid method for real-time face recognition using deep learning and visual tracking. Enterprise Information Systems, 14(9–10), 1379–1393.
Rao, Y., Lu, J. and Zhou, J. (2017) Attention-aware deep reinforcement learning for video face recognition. In Proceedings of the IEEE international conference on computer vision, pp. 3931–3940.
Liao, S., Jain, A. K., & Li, S. Z. (2013). Partial face recognition: Alignment-free approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(5), 1–14.
Manna, S., Ghildiyal, S. and Bhimani, K., (2020), Face recognition from video using deep learning. In 2020 5th International conference on communication and electronics systems (ICCES) (pp. 1101–1106). IEEE.
Huang, Z., Wang, R., Shan, S., & Chen, X. (2013). Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning Elsevier. Computer Vision and Image Understanding, 117(10), 1384–1399.
Srivastava, G., Yoder, J. A., Park, J., & Kak, A. C. (2013). Using objective ground-truth labels created by multiple annotators for improved video classification: A comparative study. Computer Vision and Image Understanding, 117(10), 1384–1399.
Zafeiriou, S., & Zhang, C. Z. A. Z. (2015). A survey on face detection in the wild: Past present and future, Elsevier. Computer Vision And Image Understanding, 138, 1–24.
Sarode, J. P., Anuse, A. D. (2014) Aframeworkfor face classification under pose variations, International Conference on Advances in Computing, Communications and Informatics (ICACCI), PP. 1886 – 1891.
Li, H., Hua, G., Shen, X., Lin, Z. and Brandt, J. (2015) Eigen-PEP for video face recognition, Springer, Computer Vision -- ACCV 2014, 9005: 17–33.
Shen, H., Zhang, J. and Zhang, H. (2015) Human action recognition by random features and hand-crafted features: A comparative study, Springer, Computer Vision - ECCV 2014 Workshops, 8926: 14–28.
Hu, X., Liao Q. and Peng, S. (2015) Video surveillance face recognition by more virtual training samples based on 3D modeling, In: Proceeding of 11th international conference on natural computation (ICNC).
Yew, C. T. and Suandi, S. A. (2011) A study on face recognition in video surveillance system using multi-class support vector machines, In: Proceeding of IEEE Region 10 Conference, TENCON.
Naderpour, H., & Mirrashid, M. (2019). Classification of failure modes in ductile and non-ductile concrete joints. Engineering Failure Analysis, 103, 361–375.
Shieh, W. -Y. and Huang, J. -C. (2009) Speedup the multi-camera video-surveillance system for elder falling detection, In: Proceeding of international conferences on embedded software and systems.
Ragashe, M. U., Goswami, M. M. and Raghuwanshi, M. M. (2015) Approach towards real time face recognition in streaming video under partial occlusion, In: Proceeding of IEEE sponsored 9th international conference on intelligent systems and control (ISCO).
Ramalingam, S. P., & Chandra Mouli, P. V. S. S. R. (2016). Two-level dimensionality reduced local directional pattern for face recognition. International Journal of Biometrics, 8(1), 52–64.
Zhou, J., Nekouie, A., Arslan, C. A., Pham, B. T., & Hasanipanah, M. (2020). Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Engineering with Computers, 36(2), 703–712.
Vinu, S. (2019). Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Communications, 104(1), 173–197.
Sundararaj, V., Muthukumar, S., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers and Security, 77, 277–288.
Vinu, S. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int. J. Intell. Eng. Syst., 9(3), 117–126.
Rejeesh, M. R. (2019). Interest point based face recognition using adaptive neuro fuzzy inference system. Multimedia Tools Applications, 78(16), 22691–22710.
Sundararaj, V. (2019). Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. International Journal of Biomedical Engineering and Technology, 31(4), 325.
Sundararaj, V., Anoop, V., Dixit, P., Arjaria, A., Chourasia, U., Bhambri, P., & MRSundararaj, R. R. (2020). CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Progress in Photovoltaics: Research and Applications, 28(11), 1128–1145.
Jose, J., Gautam, N., Tiwari, M., Tiwari, T., Suresh, A., Sundararaj, V., & Rejeesh, M. R. (2021). An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomedical Signal Processing and Control, 66, 102480.
Ganguly, S., Bhattacharjee, D., & Nasipuri, M. (2015). Wavelet and decision fusion-based 3D face recognition from range image. International Journal of Applied Pattern Recognition, 2(4), 306–324.
Nagtegaal, I. D., Odze, R. D., Klimstra, D., Paradis, V., Rugge, M., Schirmacher, P., Washington, K. M., Carneiro, F., & Cree, I. A. (2019). The 2019 WHO classification of tumours of the digestive system. Histopathology, 76(2), 182–188.
Omaima, N. A., & AL-Allaf, O. N. (2014). Review of face detection systems based artificial neural networks algorithms. International Journal of Multimedia & Its Applications, 6, 1–16.
Padey, S. (2014). Review: Face detection and recognition techniques. International Journal of Computer Science and Information Technologies, 5, 4111–4117.
Yoganand, A. V., Kavida, A. C., & Devi, D. R. (2020). Pose and occlusion invariant face recognition system for video surveillance using extensive feature set. International Journal of Biomedical Engineering and Technology, 33(3), 222–239.
Sadeghipour, E., & Sahragard, N. (2016). Face recognition based on improved SIFT algorithm. International Journal of Advanced Computer Science and Applications, 7(1), 548–551.
Medioni, G., Choi, J., Kuo, C.-H., & Fidaleo, D. (2008). Identifying non cooperative subjects at a distance using face images and inferred three-dimensional face models. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 39(1), 12–24.
Du, M., Sankaranarayanan, A. C., & Chellappa, R. (2014). Robust face recognition from multi-view videos. IEEE Transactions On Image Processing, 23(3), 1105.
Al-Obaydy, W. N. I., & Suandi, S. A. (2020). Open-set single-sample face recognition in video surveillance using fuzzy ARTMAP. Neural Computing and Applications, 32(5), 1405–1412.
Bahroun, S., Abed, R. and Zagrouba, E. (2021). KS‐FQA: Keyframe selection based on face quality assessment for efficient face recognition in video. IET Image Processing.
Ou, Z., Hu, Y., Song, M., Yan, Z., & Hui, P. (2020). Redundancy removing aggregation network with distance calibration for video face recognition. IEEE Internet of Things Journal, 8(9), 7279–7287.
Shan, X., Lu, Y., Li, Q. and Wen, Y. (2020). Model-based transfer learning and sparse coding for partial face recognition. IEEE Transactions on Circuits and Systems for Video Technology.
Afra, S., & Alhajj, R. (2020). Early warning system: From face recognition by surveillance cameras to social media analysis to detecting suspicious people. Physica A: Statistical Mechanics and its Applications, 540, 123151.
Shirley, C. P., Ram Mohan, N. R., & Chitra, B. (2021). Gravitational search-based optimal deep neural network for occluded face recognition system in videos. Multidimensional Systems and Signal Processing, 32(1), 189–215.
Chen, L., Peng, J., Liu, Z., & Zhao, R. (2017). Pricing and effort decisions for a supply chain with uncertain information. International Journal of Production Research, 55(1), 264–284.
Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I. (2018). Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48(4), 805–820.
Zhong, L., Lina, Hu., & Zhou, H. (2019). Deep learning based multi-temporal crop classification. Remote Sensing of Environment, 221, 430–443.
Ishibuchi, H., Nakashima, T., & Murata, T. (1999). Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(5), 601–618.
Chen, L., Peng, J., Zhang, Bo., & Rosyida, I. (2017). Diversified models for portfolio selection based on uncertain semivariance. International Journal of Systems Science, 48(3), 637–648.
video dataset from https://media.xiph.org/video/derf/
Wang, Y., Huang, Y. P., & Shen, X. J. (2021). ST-VLAD: Video face recognition based on aggregated local spatial-temporal descriptors. IEEE Access, 9, 31170–31178.
Hörmann, S., Cao, Z., Knoche, M., Herzog, F. and Rigoll, G., (2021), Face aggregation network for video face recognition. In 2021 IEEE international conference on image processing (ICIP) (pp. 2973–2977). IEEE.
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Gupta, G., Dwivedi, A., Rai, V. et al. Oppositional Grass Hopper Optimization with Fuzzy Classifier for Face Recognition from Video Database. Wireless Pers Commun 132, 1651–1680 (2023). https://doi.org/10.1007/s11277-023-10599-7
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DOI: https://doi.org/10.1007/s11277-023-10599-7