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-Facial expression recognition (anger, sad, happy, disgust, surprise, fear expressions) is application of advanced object detection, pattern recognition and classification task. Facial expression is one of the most powerful and natural means for human beings to show their emotions. It has found its applications in humancomputer interaction (HCI), robotics, border security systems, forensics, machine vision, video conferencing, user profiling for customer satisfaction, physiological research etc. Although humans can detect facial expressions with less effort and delay but it is still a challenge for the machine to fast and effectively detect facial expressions. Therefore algorithms should be developed to thought machines to understand facial gestures. This paper focuses on a review of different techniques for facial expression recognition.

Facial expression recognition is an interesting and challenging problem, and found in many applications like humancomputer interaction (HCI), robotics, video surveillance, border security, clinical research, person verification, crime prevention etc.. Facial expression is the movement of the muscles beneath the skin of the face. Through facial expressions human can convey their emotions without any verbal means. In this paper we have created raw database of color images. Training and testing set of images are created. Color information in an image is used to detect the face from the image. Important features from the detected face are extracted to form feature vectors using Gabor and Log Gabor filters. Principal Component Analysis (PCA) is used to reduce the dimension of the extracted features. Then these reduced features are classified using Euclidean distance. The main aim is to work upon three emotions-happy, neutral, surprise. Experiment carried out on self-generated database show comparable performance between Gabor and Log Gabor filters, where Log Gabor filters outperforming Gabor filters with classification accuracy of 86.7%.