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1. NEUTRAL FACE CLASSIFICATION USING PERSONALIZED APPEARANCE
MODELS FOR FAST AND ROBUST EMOTION DETECTION
ABSTRACT
Facial expression recognition is one of the openproblems in computer vision. Robust
neutral face recognition inreal time is a major challenge for various supervised learningbased
facial expression recognition methods. This is due to thefact that supervised methods cannot
accommodate allappearance variability across the faces with respect to race, pose,lighting, facial
biases, etc. in the limited amount of training data.Moreover, processing each and every frame to
classify emotionsis not required, as user stays neutral for majority of the time inusual
applications like video chat or photo album/web browsing.Detecting neutral state at an early
stage, thereby bypassing thoseframes from emotion classification would save thecomputational
power. In this work, we propose a light-weightneutral vs. emotion classification engine, which
acts as a pre-processerto the traditional supervised emotion classificationapproaches. It
dynamically learns neutral appearance at KeyEmotion (KE) points using a textural statistical
model,constructed by a set of reference neutral frames for each user.The proposed method is
made robust to various types of userhead motions by accounting for affine distortions based on a
textural statistical model. Robustness to dynamic shift of KEpoints is achieved by evaluating the
similarities on a subset ofneighborhood patches around each KE point using the prior
information regarding the directionality of specific facial actionunits acting on the respective KE
point. The proposed method,as a result, improves ER accuracy and simultaneously
reducescomputational complexity of ER system, as validated onmultiple databases.