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.

监视视频中的面部日志检测（FLD）从视频序列（VS）中提取新的面部图像。FLD利用生物识别技术来识别人类。为了以更少的复杂性来提高精确的FLD，我们所使用的方法是Prewitt Logistic深度递归神经学习（PLDRNL）。从视频数据库接收到输入VS。接下来，从VS中提取关键帧。该提议的深度递归神经学习方法使用四个隐藏层以边缘的形式去除面部特征，例如面部，眼睛，鼻子和嘴巴。每个元素的边缘都使用Prewitt边缘检测器通过水平和垂直蒙版得出。最后，相关要素被输入到输出层。PLDRNL在输出层使用逻辑激活函数，以将提取的相关元素与预存储的测试特征向量进行匹配。如果两个特征匹配，则检测到给定VF中的人脸。使用输出层的梯度下降功能可将FD中的误差降至最低。根据结果​​，人脸可以有效地识别出最低的假阳性率（FPR）。使用FLD，FPR和时间复杂度等不同因素进行实验评估。