A STUDY ON FACTORS AFFECTING FACIAL RECOGNITION ACCURACY
Keywords:
Face detection, Face recognitionAbstract
The system of biometric security is very essential for providing safety and security to people against frauds, theft etc. One of the most essential and secured biometric system is Face recognition; It can be used for identification and surveillance to prove the identity of a person and spot individuals. The aim of this analysis is to study some factors which affect the perfection and accuracy of the face recognition. The outcomes we get that regardless of the continuous research, a perfect face recognition system is needed that can perform its functions in natural as well as unnatural environment.
References
Khan, A., et al.: Forensic video analysis: passive tracking system for automated Person of Interest (POI) localization. IEEE Access 6, 43392–43403. (2018).
Chawla, D., Trivedi, M.C.: A comparative study on face detection techniques for security surveillance. In: Advances in Computer and Computational Sciences, pp. 531–541 (2018).
Abdullah, N.A., et al.: Face recognition for criminal identification: an implementation of principal component analysis for face recognition. In: The 2nd International Conference on Applied Science and Technology 2017.
Dhamija, J., Choudhary, T., Kumar, P., Rathore, Y.S.: An advancement towards efficient face recognition using live video feed. In: International Conference on Computational Intelligence and Networks (CINE), pp. 53–56 (2017).
Kavitha, J., Mirnalinee, T.T.: Automatic frontal face reconstruction approach for pose invariant face recognition. In: Proceedings of the 4th International Conference on Recent Trends in Computer Science & Engineering, Elsevier, vol. 87, pp. 300–305 (2016).
Gao, Y., Jong, H.: Cross-pose face recognition based on multiple virtual views and alignment error. Pattern Recognit. Lett. 65, 170–176 (2015).
Ahonen, T., Hadid, A., Pietika, M.: Face description with local binary patterns: application toface recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Sharif, M., Naz, F., Yasmin, M., Shahid, M.A., Rehman, A.: Face recognition: a survey. J. Eng.Sci. Technol. Rev. 10(2), 166–177 (2017).
Luo, Z., Hu, J., Deng, W., Shen, H.: Deep unsupervised domain adaptation for face recognition.In: 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 453–457 (2018).
Zhou, H., Lam, K.: Age-invariant face recognition based on identity inference from appearanceage. J. Pattern Recognit. 76, 191–202 (2018).
Gosavi, V.R., Sable, G.S., Deshmane, A.K.: Evaluation of feature extraction techniques using neural network as a classifier: a comparative review for face recognition. Int. J. Sci. Res. Sci. Technol. 4(2), 1082–1091 (2018).
Fu, T., Chiu, W., Wang, Y.F.: Learning guided convolutional neural networks for cross-resolution face recognition. In: 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) (2017).
Bhavani, K., et al.: Real time face detection and recognition in video surveillance. Int. Res. J. Eng. Technol. 4(6), 1562–1565 (2017).
