Neutral face classification using personalized appearance models for fast and robust emotion detection

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Engineering

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.

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Face recognition is a computer application technique for automatically identifying or verifying a person from a digital image or a video frame source. To do this is by comparing selected facial features from the digital image and a face dataset. It is basically used in security systems and can be compared to other biometrics such as fingerprint recognition or eye, iris recognition systems. The main limitation of the current face recognition system is that they only detect straight faces looking at the camera. Separate versions of the system could be trained for each head orientation, and the results can be combined using arbitration methods similar to those presented here. In earlier work, the face position must be centerlight position; any lighting effect will affect the system. Similarly the eyes of person must be open and without glass.

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Neutral face classification using personalized appearance models for fast and robust emotion detection

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.

Neutral face classification using personalized appearance models for fast and robust emotion detection

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