Kartheek Mukku Nisanth, Prasad Munaga V N K, Bhukya Raju
Institute for Development and Research in Banking Technology, Hyderabad, India.
Department of Computer Science and Engineering, National Institute of Technology, Warangal, India.
Cogn Neurodyn. 2024 Apr;18(2):317-335. doi: 10.1007/s11571-022-09824-z. Epub 2022 Jun 25.
Facial expressions can convey the internal emotions of a person within a certain scenario and play a major role in the social interaction of human beings. In automatic Facial Expression Recognition (FER) systems, the method applied for feature extraction plays a major role in determining the performance of a system. In this regard, by drawing inspiration from the Swastik symbol, three texture based feature descriptors named Symbol Patterns (SP, SP and SP) have been proposed for facial feature extraction. SP generates one pattern value by comparing eight pixels within a 33 neighborhood, whereas, SP and SP generates two pattern values each by comparing twelve and sixteen pixels within a 55 neighborhood respectively. In this work, the proposed Symbol Patterns (SP) have been evaluated with natural, fibonacci, odd, prime, squares and binary weights for determining the optimal recognition accuracy. The proposed SP methods have been tested on MUG, TFEID, CK+, KDEF, FER2013 and FERG datasets and the results from the experimental analysis demonstrated an improvement in the recognition accuracy when compared to the existing FER methods.
面部表情能够在特定场景中传达一个人的内在情绪，并且在人类的社交互动中发挥着重要作用。在自动面部表情识别（FER）系统中，用于特征提取的方法在决定系统性能方面起着主要作用。在这方面，通过从卍字符获取灵感，提出了三种基于纹理的特征描述符，即符号模式（SP、SP和SP）用于面部特征提取。SP通过比较3×3邻域内的八个像素生成一个模式值，而SP和SP分别通过比较5×5邻域内的十二个和十六个像素各自生成两个模式值。在这项工作中，所提出的符号模式（SP）已经用自然、斐波那契、奇数、质数、平方和二进制权重进行了评估，以确定最佳识别准确率。所提出的SP方法已经在MUG、TFEID、CK+、KDEF、FER2013和FERG数据集上进行了测试，实验分析结果表明，与现有的FER方法相比，识别准确率有所提高。
