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Component, Facial Expressions, Histogram of Oriented Gradients (HOG), Support Vector Machine (SVM), Facial Expression Recognition, HOG Features, Facial Component Detection.

Facial expression is one of the most commonly used nonverbal means by humans to transmit internal emotional states and, therefore, it plays a fundamental role in interpersonal interactions. Although there is a wide range of possible facial expressions, psychologists have identified six fundamental ones (happiness, sadness, surprise, anger, fear and disgust) that are universally recognized. Automatic facial expression recognition (FER) is a topic of growing interest mainly due to the rapid spread of assistive technology applications, as human–robot interaction, where a robust emotional awareness is a key point to best accomplish the assistive task. The proposed work aims to design a robust facial expression recognition system (FER). FER system can be divided into three modules, namely facial registration, feature extraction and classification. The objective of this work is the recognition of facial expressions based the Histogram of Oriented Gradients (HOG) and support vector machine (SVM) algorithm.

Firstly, a comprehensive study on the application of histogram of oriented gradients HOG descriptor in the FER problem is presented, highlighting as this powerful technique could be effectively exploited for this purpose. Then, a multi SVM is then trained to perform the facial expression classification.

The proposed technique is applied to two public datasets, such as the JAFFE dataset and an extended Cohn-Kanade (CK+) dataset. Facial expression recognition from the proposed method are validated and the True Success Rate (TSR) for the test data available is evaluated, and then a comparative study versus existing techniques is presented. Face Recognition using HOG and SVM are better compared to existing state of the art methods.