Face Recognition using local Patterns

Deriving an effective face representation is very essential task for automatic face recognition application. In this paper we used a feature descriptor called the Local Directional Number Pattern (LDN), which allows individual’s face recognition under different lightning’s, pose and expressions. Face recognition deals with different challenging problems in the field of image analysis and human computer interface. To deal with attention in our proposed work we use local patterns, a local directional number pattern (LDN) method, a six bit compact code for face recognition and understanding. By using LDN method we encode the directional information of the face images by convolving the face image with the compass mask. This compass mask extracts the edge response values in eight directions in the neighborhood. For each pixel we get the maximum and the minimum directional values which generate a LDN code i.e. generating an LDN image. Later LDN image is divided into number of blocks for each block histogram is computed and finally adds these histogram from each block to form the feature vector which acts as face descriptor to represent the face images. We perform different experiments under various illumination, pose and expression conditions

A facial depression recognition method based on hybrid multi-head cross attention network

IntroductionDeep-learn methods based on convolutional neural networks (CNNs) have demonstrated impressive performance in depression analysis. Nevertheless, some critical challenges need to be resolved in these methods: (1) It is still difficult for CNNs to learn long-range inductive biases in the low-level feature extraction of different facial regions because of the spatial locality. (2) It is difficult for a model with only a single attention head to concentrate on various parts of the face simultaneously, leading to less sensitivity to other important facial regions associated with depression. In the case of facial depression recognition, many of the clues come from a few areas of the face simultaneously, e.g., the mouth and eyes.MethodsTo address these issues, we present an end-to-end integrated framework called Hybrid Multi-head Cross Attention Network (HMHN), which includes two stages. The first stage consists of the Grid-Wise Attention block (GWA) and Deep Feature Fusion block (DFF) for the low-level visual depression feature learning. In the second stage, we obtain the global representation by encoding high-order interactions among local features with Multi-head Cross Attention block (MAB) and Attention Fusion block (AFB).ResultsWe experimented on AVEC2013 and AVEC2014 depression datasets. The results of AVEC 2013 (RMSE = 7.38, MAE = 6.05) and AVEC 2014 (RMSE = 7.60, MAE = 6.01) demonstrated the efficacy of our method and outperformed most of the state-of-the-art video-based depression recognition approaches.DiscussionWe proposed a deep learning hybrid model for depression recognition by capturing the higher-order interactions between the depression features of multiple facial regions, which can effectively reduce the error in depression recognition and gives great potential for clinical experiments

Hybrid image representation methods for automatic image annotation: a survey

In most automatic image annotation systems, images are represented with low level features using either global methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is beneficial in annotating images. In this paper, we provide a survey on automatic image annotation techniques according to one aspect: feature extraction, and, in order to complement existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation