A major challenge in pattern recognition is labeling of large numbers of samples. This problem has been solved by extending supervised learning to semi-supervised learning. Thus semi-supervised learning has become one of the most important methods on the research of facial expression recognition. Frontal and un-occluded face images have been well recognized using traditional facial expression r...

Despite of achieving good performance in controlled environment, the conventional 3D face recognition systems still encounter problems in handling the large variations in lighting conditions, facial expression and head pose The humans use the hybrid approach to recognize faces and therefore in this proposed method the human face recognition ability is incorporated by combining global and local ...

Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing regions, one question remains unanswered: what is the impact of these on subsequent related tasks? In this paper, focus put expression recognition (FER), where landmarks used face registration, which a common usage. Since most datasets do not allow proper measurement performance accordin...

In recent years, emotion recognition has attracted extensive interest in signal processing, artificial intelligence and pattern recognition due to its potential applications to human-computer-interaction (HCI). Most previously published works in the field of emotion recognition devote to performing emotion recognition by using either affective speech or facial expression. However, Affective spe...

Facial micro-expression is a superposition of features and identity information subject. For the problem interference in recognition, this study proposes new method for facial recognition by de-identity information, called two-stream difference network (TSDN). First, encoder-decoder trained convolutional neural network, where input stream image, image. The image apex onset sequence. are recorde...