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ENHANCING FACIAL EXPRESSION RECOGNITION WITH SYMBOLIC AGGREGATE APPROXIMATION AND LOCAL BINARY PATTERN COMBINATION
Aadil Rahiman , Research Scholar, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, IndiaAbstract
Facial expression recognition plays a crucial role in various applications, including human-computer interaction and affective computing. This study proposes a novel approach to enhance automatic facial expression recognition using a combination of Symbolic Aggregate approXimation (SAX) and Local Binary Pattern (LBP) techniques. SAX transforms time series data into symbolic sequences, simplifying the complexity of facial expression dynamics. LBP extracts local texture features from facial images, capturing subtle variations in expression. By integrating SAX with LBP, this approach achieves robust representation of both temporal and spatial facial dynamics, enhancing the accuracy and efficiency of facial expression recognition systems. Experimental results demonstrate the effectiveness of the proposed method in recognizing diverse facial expressions across different datasets.
Keywords
Facial expression recognition, Symbolic Aggregate approXimation (SAX), Local Binary Pattern (LBP)
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Copyright (c) 2024 Aadil Rahiman
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