RETRACTED ARTICLE: Prewitt Logistic Deep Recurrent Neural Learning for Face Log Detection by Extracting Features from Images

Abstract

Face log detection (FLD) in the surveillance video extracts a new face image from the video sequences (VS). FLD utilizes biometric techniques for humans’ recognition. To improve the precise FLD with less complexity of our proposed method is Prewitt Logistic Deep Recurrent Neural Learning (PLDRNL) used. The input VS was received from the video database. Next, the keyframes are extracted from the VS. This proposed deep recurrent neural learning method uses four hidden layers to remove the facial features such as the face, eyes, nose, and mouth in the form of an edge. The edges of each element are derived using the Prewitt edge detector through the horizontal and vertical mask. Finally, the relevant features are fed into the output layer. The PLDRNL uses a logistic activation function at the output layer for matching the extracted related elements with the pre-stored testing feature vector. If two features are matched, then the face in the given VF is detected. The error in the FD is minimized using gradient descent function at the output layer. Based on the results, the human face effectively identified with the minimum false-positive rate (FPR). Experimental evaluation is carried out using different factors such as FLD, FPR, and time complexity.