Local Zernike Moment Representation for Facial Affect Recognition (original) (raw)

Automatic analysis of facial affect: A survey of registration, representation and recognition

Abstract—Automatic affect analysis has attracted great interest in various contexts including the recognition of action units and basic or non-basic emotions. In spite of major efforts, there are several open questions on what the important cues to interpret facial expressions are and how to encode them. In this paper, we review the progress across a range of affect recognition applications to shed light on these fundamental questions. We analyse the state-of-the-art solutions by decomposing their pipelines into fundamental components, namely face registration, representation, dimensionality reduction and recognition. We discuss the role of these components and highlight the models and new trends that are followed in their design. Moreover, we provide a comprehensive analysis of facial representations by uncovering their advantages and limitations, we elaborate on the type of information they encode and discuss how they deal with the key challenges of illumination variations, registration errors, head-pose variations, occlusions and identity bias. This survey allows us to identify open issues and to define future directions for designing real-world affect recognition systems.

Local feature extraction based facial emotion recognition: A survey

International Journal of Electrical and Computer Engineering (IJECE), 2020

Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of Local Binary Pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively.

A Brief Survey on Facial Emotion Recognition

international journal for research in applied science and engineering technology ijraset, 2020

This paper introduces a series of methodologies to accurately tackle the classification of emotion and compare the various methodologies and identify the ideal solution. The progression of the decades of scientific research has been conducted for developing methods for automated emotion recognition. Now, there is an extensive literature proposing and evaluating various methods, leveraging techniques from multiple fields, such as signal processing, computer vision, machine learning, and speech processing. Given an image of arbitrary size, the job is to identify an emotion of a human face appearing in the image. Face detection in complex environments is disputing since the faces may appear in different scales, different head poses, and orientations. External factors also play a vital role; for instance, the lighting conditions, facial expressions, and shadows are few other sources of variations that need to be taken into account. The approach is to yield a better classification performance implemented in real-world scenarios with fewer exemptions and more generalized accuracy.