Author Topic: Facial Expression Analysis: Towards Optimizing Performance and Accuracy  (Read 1713 times)

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Author : Sujay Agarkar, Ayesha Butalia, Romeyo D’Souza, Shruti Jalali, Gunjan Padia
International Journal of Scientific & Engineering Research Volume 2, Issue 6, June-2011
ISSN 2229-5518
Download Full Paper : PDF

Abstract— Facial expressions play an important role in interpersonal relations. This is because humans demonstrate and convey a lot of evident information visually rather than verbally. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine remains a challenge as of today. To automate recognition of facial expressions, machines must be taught to understand facial gestures. In sustenance to this idea, we consider a facial expression to consist of deformations of facial components and their spatial relations, along with changes in the pigmentation of the same. This paper envisages interpretation of relative deviations of facial components, leading to expression recognition of subjects in images. Many of the potential applications utilizing automated facial expression analysis will necessitate speedy performance. We propose approaches to optimize the performance and accuracy of such a system by introducing ways to personalize and calibrate the system. We also discuss potential problems that may arise to hinder the accuracy, and suggest strategies to deal with them.
Index Terms—Facial Gestures, Action Units, Neuro-Fuzzy Networks, Fiducial Points, Missing Values, Calibration.

1   INTRODUCTION                                                                     
FACIAL expression analysis and recognition is a basic process performed by every human every day. Each one of us analyses the expressions of the individuals we interact with, to understand best their response to us. Even an infant can tell his/her mothers smile from her frown. This is one of the very fundamental communica-tion mechanisms known to man.
In the next step to Human-Computer interaction, we endeavor to empower the computer with this ability — to be able to discern the emotions depicted on a person’s visage. This seemingly effortless task for us needs to be broken down into several parts for a computer to per-form. For this purpose, we consider a facial expression to represent fundamentally, a deformation of the original features of the face.

On a day-to-day basis, humans commonly recognize emotions by characteristic features displayed as part of a facial expression. For instance, happiness is undeniably associated with a smile, or an upward movement of the corners of the lips. This could be accompanied by upward movement of the cheeks and wrinkles directed outward from the outer corners of the eyes. Similarly, other emotions are characterized by other deformations typical to the particular expression.
More often than not, emotions are depicted by subtle changes in some facial elements rather than their obvious contortion to represent its typical expression as is defined. In order to detect these slight variations induced, it is important to track fine-grained changes in the facial features.
The general trend of comprehending observable com-ponents of facial gestures utilizes the FACS, which is also a commonly used psychological approach. This system, as described by Ekman [12], interprets facial information in terms of Action Units, which isolate localized changes in features such as eyes, lips, eyebrows and cheeks.
The actual process is akin to a divide-and-conquer approach, a step-by-step isolation of facial features, and then recombination of the interpretations of the same in order to finally arrive at a conclusion about the emotion depicted.

Visually conveyed information is probably the most important communication mechanism used for centuries, and even today. As mentioned by Mehrabian [8], upto 55% of the communicative message is understood through facial expressions. This understanding has sparked an enormous amount of speculation in the field of facial gestural analysis over the past couple of decades. Many different techniques and approaches have been proposed and implemented in order to simplify the way computers comprehend and interact with their users.
The need for faster and more intuitive Human-Computer Interfaces is ever increasing with many new innovations coming to the forefront. [1]
Azcarate et al. [11] used the concept of Motion Units (MUs) as input to a set of classifiers in their solution to the facial emotion recognition problem. Their concept of MUs is similar to “Action Units” as described by Ek-man. [12]. Chibelushi and Bourel [3] propose the use of GMM (Gaussian Mixture Model) for pre-processing and HMM (Hidden Markov Model) with Neural Networks for AU identification.
Lajevardi and Hussain [15] suggest the idea of dynamically selecting a suitable subset of Gabor filters from the available 20 (called Adaptive Filter Selection), depending on the kind of noise present. Gabor Filters have also been used by Patil et. Al [16]. Lucey et. al [17] have devised a method to detect expressions invariant of registration using Active Appearance Models (AAM). Along with multi-class SVMs, they have used this method to identify expressions that are more generalized and independent of image processing constraints such as pose and illumination. This technique has been used by Borsboom et. al [7] feature extraction, whereas they have used Haar-like features to perform face detection.
Noteworthy is the work of Theo Gevers et. al [21] in this field. Their facial expression recognition approach enhances the AAMs mentioned above, as well as MUs. Tree-augmented Bayesian Networks (TAN), Native Bayes (NB) Classifiers and Stochastic Structure Search (SSS) algorithms are used to effectively classify motion detected in the facial structure dynamically.
Similar to the approach adopted in our system, P. Li et. al have utilized fiducial points to measure feature deviations and OpenCV detectors for face and feature detection. Moreover, their geometric face model orientation is akin to our approach of polar transformations for processing faces rotated within the same plane (i.e. for tilted heads).

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