Face recognition under unconstrained environment for videos from internet

To overcome the limitation of unconstrained environment in face recognition, a modified algorithm using curvelet and DCNN (Madarkar and Sharma in J Intell Fuzzy Syst 38(5):6423–6435, 2020) is proposed. First, the curvelet transform of the face image is taken to obtain low frequency and high frequency components and DCNN are trained to make the image more robust to background and other changes. This results in a general feature vector and result is obtained by weighted fusion of all the DCNN. Finally the generalized CDCNN is used with random weights. The efficiency has been significantly improved by combining curvelet with DCNN for approximate band detailed sub band to improve the training and testing accuracy. DCNN is a well established technique for face recognition and is insensitive to small changes in input data. It possesses advantage of being ineffective to outliers and fast learning rate. The proposed method is robust-to-variation of imaging conditions. Performance comparison with other existing techniques shows that the proposed technique provides better results in terms of false acceptance rate, false rejection rate and recognition accuracy.

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