Facial expression recognition is an extensive aspect in the field of pattern recognition and affective computing. Recognizing emotions by facial expression is an imperative action to design control-oriented and human computer interactive applications. Facial expression recognition is probable by the motion of facial muscles resulting in the appearance variation of face features. Accurate feature extraction is one of the extreme challenges that should be scrutinized for an admirable facial expression recognition system. One of the extensive key techniques used for feature extraction mechanism in facial expression recognition is wavelet transform. The features extracted from the wavelet transform incorporate both spatial and spectral domain information which is best adequate for identifying human emotions through facial expressions. In this paper, the statistical parameters from the proposed subband selective multilevel stationary biorthogonal wavelet transform are estimated and are used as features for effective recognition of emotion. The potency of the feature extraction algorithm is boosted by calculating the mean and maximum local energy wavelet subband of stationary biorthogonal wavelet transform. SVM classifier is used for classification of emotion using the preferred chosen features. Protracted experiments with well-known database for facial expression such as JAFEE database, CK + database, FEED database, SFEW database and RAF database demonstrates a better promising results in emotion classification.

面部表情识别是模式识别和情感计算领域的一个广泛方面。通过面部表情识别情绪是设计面向控制和人机交互应用程序的必要措施。面部肌肉的运动可能导致面部表情识别，从而导致面部特征的外观变化。准确的特征提取是令人钦佩的面部表情识别系统应审查的极端挑战之一。小波变换是用于面部表情识别中的特征提取机制的广泛关键技术之一。从小波变换提取的特征结合了空间和光谱域信息，这最适合通过面部表情识别人的情绪。在本文中，从提出的子带选择性多级平稳双正交小波变换估计统计参数，并将其用作有效识别情绪的特征。通过计算平稳双正交小波变换的平均和最大局部能量小波子带，可以提高特征提取算法的效率。SVM分类器用于使用首选特征对情感进行分类。使用著名的面部表情数据库（例如JAFEE数据库，CK +数据库，FEED数据库，SFEW数据库和RAF数据库）进行的长时间实验证明，在情感分类方面有更好的前景。