Survey on real‐time facial expression recognition techniques

Abstract

Cameras constantly capture and track facial images and videos on cell phones, webcams etc. In the past decade, facial expression classification and recognition has been the topic of interest as facial expression analysis has a wide range of applications such as intelligent tutoring system, systems for psychological studies etc. This study reviews the latest advances in the algorithms and techniques used in distinct phases of real-time facial expression recognition. Though there are state-of-art approaches to address facial expression identification in real-time, many issues such as subjectivity-removal, occlusion, pose, low resolution, scale, variations in illumination level and identification of baseline frame still remain unaddressed. Attempts to deal with such issues for higher accuracy lead to a trade-off in efficiency. Furthermore, the goal of this study is to elaborate on these issues and highlight the solutions provided by the current approaches. This survey has helped the authors to understand that there is a need for a better strategy to address these issues without having to trade-off performance in real-time.

Fingerprint Quality Assessment: Matching Performance and Image Quality

“…Generally, this kind of measure is to observe the similarity between two different variables such as wavelet coefficients. In this case, to compare the studied metrics, we simply provide experiment results of two evaluation approaches, one is a validation approach relied on Enrollment Selection (ES) [27] and another is an evaluation method with multiple bins of sorted biometric samples [7].…”

“…To validate a biometric quality metric, an objective index [27] is used for representing the quality of a sample. The objective measure is an offline sample EER (SEER) value calculated from a set of intra-class matching scores and a set of inter-class matching scores formulated as N − 1 genuine matching scores (GMS)…”

Bimodal recognition of affective states with the features inspired from human visual and auditory perception system

“…Ekman's extensive studies have classified human's face expressions into seven categories, namely happiness, anger, sadness, neutral, fear, surprise and disgust. The key techniques with impressive performances for emotion recognition using facial features can be grouped into appearance and geometric‐based features . The appearance feature is defined by the facial texture and shape or location of facial points, defined by geometric features.…”

“…The key techniques with impressive performances for emotion recognition using facial features can be grouped into appearance and geometric-based features. 7 The appearance feature is defined by the facial texture and shape or location of facial points, defined by geometric features. Ekman and Friesen 8 developed a facial expression coding system to code facial expressions by the combination of action units following a set of predefined rules.…”

Facial Emotion Recognition Using NLPCA and SVM

“…For extracting features geometric-based extraction methods [5] and appearance-based extraction methods [6] are used. The former methods extract location and shape-related metrics from the eyes, eyebrows, mouth, and nose.…”