A face recognition algorithm based on feature fusion

Abstract

In the process of building a smart city, face recognition can be applied to the transformation of enterprises, communities, and parks. The combination of building security system and face recognition technology can improve the security experience of enterprises and citizens through the solution of hardware and software integration. Face recognition is still facing the challenges of illumination, occlusion, and attitude change in the actual application process. In addition, the end-to-end convolutional neural networks (CNN) seldom make use of the hierarchical feature of the network. So, we propose a hierarchy feature fusion method for face recognition, which uses supervisory information to learn shallow and deep facial features. The features are fused to enhance the recognition accuracy of face recognition against illumination and occlusion. The method is applied to the transformation of the visual geometry group network and Lightened CNN. The face recognition experiments are carried out using the hierarchy network. Our method has achieved good recognition results in the labeled faces in the wild (LFW) and AR face databases.

Multimodal Hierarchical CNN Feature Fusion for Stress Detection

“…A face recognition algorithm with hierarchical feature fusion was proposed by Zhang et al [41]. The proposed framework learned shallow and deep facial aspects using supervisory information.…”

“…Due to these reasons, in recent studies, feature fusion methodologies like hierarchical features at each level are integrated and used for training. The relevant information can be retained, and information loss is minimized by this hierarchical feature fusion [41]. In most end-to-end CNN networks, the last convolution layer's feature maps, mainly global features without hierarchy features, serve as discriminative features.…”

“…Initially, frequency domain features from EDA and ECG frequency bands or the raw frequency domain data are given as input to the CNN model. Inspired by the effectiveness of hierarchical feature fusion on CNN from the literature's [38], [39], [40], [41], [42], we concatenate the high, mid, and low-level features from the convolutional layers of EDA and ECG separately to form a hierarchical feature set. Unlike single-level fusion, gradual fusion has shown better performance [43].…”

Detecting Double JPEG Compressed Color Images via an Improved Approach

“…To extract features from the rounding error and the truncation error, a specific CNN will be applied in our experiment. Although many models can be applied [34][35], there are three advantages of CNN. Firstly, owing to the properties of the convolution and pooling computations, it is possible that the translation of the image has no effect on the final feature vector.…”

“…Double JPEG compression can be classified by different standards. For instance, based on whether the quality factors are the same, double JPEG compression is divided into two categories: one of them is with different quantization matrices and the other is with the same quantization matrix [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Fig.…”