Badi Mame Antoine, Tapamo Jules-Raymond
Discipline of Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa.
PeerJ Comput Sci. 2023 Jun 28;9:e1388. doi: 10.7717/peerj-cs.1388. eCollection 2023.
An important task in automatic facial expression recognition (FER) is to describe facial image features effectively and efficiently. Facial expression descriptors must be robust to variable scales, illumination changes, face view, and noise. This article studies the application of spatially modified local descriptors to extract robust features for facial expressions recognition. The experiments are carried out in two phases: firstly, we motivate the need for face registration by comparing the extraction of features from registered and non-registered faces, and secondly, four local descriptors (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Compound Local Binary Patterns (CLBP), and Weber's Local Descriptor (WLD)) are optimized by finding the best parameter values for their extraction. Our study reveals that face registration is an important step that can improve the recognition rate of FER systems. We also highlight that a suitable parameter selection can increase the performance of existing local descriptors as compared with state-of-the-art approaches.
自动面部表情识别（FER）中的一项重要任务是有效且高效地描述面部图像特征。面部表情描述符必须对可变尺度、光照变化、面部视角和噪声具有鲁棒性。本文研究空间修改局部描述符在提取用于面部表情识别的鲁棒特征方面的应用。实验分两个阶段进行：首先，通过比较从已注册和未注册面部提取特征，我们激发了面部配准的必要性；其次，通过找到四种局部描述符（方向梯度直方图（HOG）、局部二值模式（LBP）、复合局部二值模式（CLBP）和韦伯局部描述符（WLD））提取的最佳参数值来对其进行优化。我们的研究表明面部配准是可以提高FER系统识别率的重要步骤。我们还强调，与现有最先进方法相比，合适的参数选择可以提高现有局部描述符的性能。
