Paper Details

Facial expression is the movement of the muscles beneath the skin of the face. Through facial expressionshuman can convey their emotions without any verbal means. In this paper we have study for raw database of colorimages; Training and testing set of images are created. Color information in an image is used to detect the face from theimage. Important features from the detected face are extracted to form feature vectors using Gabor and Log Gaborfilters. Principal Component Analysis (PCA) is used to reduce the dimension of the extracted features. Then thesereduced features are classified using Euclidean distance. The main aim is to work upon three emotions-happy, neutral,surprise. Facial expression recognition (anger, sad, happy, disgust, surprise, fear expressions) is application of patternrecognition and classification task. Through facial expression human beings can show their emotions. Its applicationsare in human-computer interaction (HCI), robotics, border security systems, forensics, video conferencing, user profilingfor customer satisfaction, physiological research etc. This paper presented a Facial Expression Recognition systembased using Log Gabor Filter and PCA. Euclidean distance is used as a classifier. The proposed system is designed andtested with FEI database. Two emotions to be recognized are happy and neutral.

Mr. Nitin V. Narkhede, Asst. Prof. Priti S.Subraamanium, Prof. Dinesh.D.Patil, “Survey on an Accurate Facial Emotion Recognition System Based On PCA”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 1, pp. 126-131, January 2017.