Facial emotion detection using modified eyemap–mouthmap algorithm on an enhanced image and classification with tensorflow

• Published in: The Visual computer Vol. 36; no. 3; pp. 529 - 539
• Main Authors: Joseph, Allen, Geetha, P.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Approximation; Artificial Intelligence; Central processing units; Classification; Computer Graphics; Computer Science; CPUs; Discrete Wavelet Transform; Emotion recognition; Emotions; Face recognition; Geometry; Image classification; Image enhancement; Image Processing and Computer Vision; Input output; Methods; Mouth; Neural networks; Original Article; Support vector machines; Triangles; Wavelet transforms; Emotion; Eyemap; Facial geometry; Mouthmap; Facial expression
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-019-01628-3

Detection of emotion using facial expression is a growing field of research. Facial expression detection is also helpful to identify the behavior of a person when a man interacts with the computer. In this work, facial expression recognition with respect to the changes in the facial geometry is proposed. First, the image is enhanced by means of discrete wavelet transform and fuzzy combination. Then, the facial geometry is found using the modified eyemap and mouthmap algorithm after finding the landmarks. Finally, the area and angle of the constructed triangles are found and classified using neural network with the help of tensorflow central processing unit version. Results show that the proposed algorithm is efficient in finding the facial emotion.