Recognition of facial expressions based on salient geometric features and support vector machines

Abstract

Facial expressions convey nonverbal cues which play an important role in interpersonal relations, and are widely used in behavior interpretation of emotions, cognitive science, and social interactions. In this paper we analyze different ways of representing geometric feature and present a fully automatic facial expression recognition (FER) system using salient geometric features. In geometric feature-based FER approach, the first important step is to initialize and track dense set of facial points as the expression evolves over time in consecutive frames. In the proposed system, facial points are initialized using elastic bunch graph matching (EBGM) algorithm and tracking is performed using Kanade-Lucas-Tomaci (KLT) tracker. We extract geometric features from point, line and triangle composed of tracking results of facial points. The most discriminative line and triangle features are extracted using feature selective multi-class AdaBoost with the help of extreme learning machine (ELM) classification. Finally the geometric features for FER are extracted from the boosted line, and triangles composed of facial points. The recognition accuracy using features from point, line and triangle are analyzed independently. The performance of the proposed FER system is evaluated on three different data sets: namely CK+, MMI and MUG facial expression data sets.

Facial Expression Recognition Based on Local Fourier Coefficients and Facial Fourier Descriptors

“…In both cases, our proposal obtains the highest recognition accuracy. This occurs, even when some approaches don't use the complete data set of MUG, like [7], and the process is based on sequence of frames, as in [11]. It is worth noting that the TFEID data set presents a bigger challenge for FER because instead of CK+ and MUG, the facial expressions are shown only by Taiwanese people.…”

“…On the other hand, the methods applied for geometric features are AAM [10], EBGM [11], concatenation [12] and straight-line distances [13] of fiducial points. It is worth noting that survey papers ( [3] and [4]) mention that approaches which combine appearance and geometric features reach higher accuracy performance, for example [14] and [15].…”

Intelligent Real-Time Facial Expression Recognition from Video Sequences based on Hybrid Feature Tracking Algorithms

“…This paper considers mainly real-time video based FER, standard dataset for realtime video facial expression are used for testing and evaluating. Moreover, the proposed system is designed to recognize the most six effective expressions of Human face; anger, disgust, fear, happiness, sadness, and surprise [1,2,3].…”

“…An automatic FER system usually consists of three steps [1,2]: i) Face detection, ii) Facial feature extraction, iii) Facial expression recognition. Each step of them is considered a separate research area and has its own challenges.…”

“…A high level of accuracy and a few milliseconds processing time are the main goal of such system. Many FER systems have been developed to satisfy these goals [1,2,3,4]. However, some limitations still exist.…”