Face Recognition Using $Sf_{3}CNN$ With Higher Feature Discrimination
This work provides an incremental improvement in face recognition accuracy for video-based systems, which is beneficial for applications requiring robust identification in dynamic environments.
This paper addresses the challenge of face recognition in real-world video conditions by proposing the $Sf_{3}CNN$ framework. The framework achieves an accuracy of 99.10% on the CVBL video database, improving upon the previous 97% on the same database using 3D ResNets.
With the advent of 2-dimensional Convolution Neural Networks (2D CNNs), the face recognition accuracy has reached above 99%. However, face recognition is still a challenge in real world conditions. A video, instead of an image, as an input can be more useful to solve the challenges of face recognition in real world conditions. This is because a video provides more features than an image. However, 2D CNNs cannot take advantage of the temporal features present in the video. We therefore, propose a framework called $Sf_{3}CNN$ for face recognition in videos. The $Sf_{3}CNN$ framework uses 3-dimensional Residual Network (3D Resnet) and A-Softmax loss for face recognition in videos. The use of 3D ResNet helps to capture both spatial and temporal features into one compact feature map. However, the 3D CNN features must be highly discriminative for efficient face recognition. The use of A-Softmax loss helps to extract highly discriminative features from the video for face recognition. $Sf_{3}CNN$ framework gives an increased accuracy of 99.10% on CVBL video database in comparison to the previous 97% on the same database using 3D ResNets.