Abstract
Face is the gateway to mind. Understanding facial expressions gives the affective state of human mind. Accurate detection of facial expressions is one of the open challenges in machine vision. The fulcrum of facial expression detection and classification depends on extraction of salient points on the face. The accurate detection of these points is a mandatory step for emotion detection as these facial points undergo tremendous change as emotion expressed by human changes. This paper proposes an accurate adaptive algorithm for extraction of the distinct facial points which will make identification of various expressions a feasible task. Viola–Jones face detection algorithm was used for detection of face. The extracted face region was decimated into various regions of interest (ROIs) based on prior knowledge of human face anatomy. Eye centers were located using horizontal and vertical projections of histogram of eye ROI. The ROI for eyebrows was located using the location of eye centers. The eyebrows were detected by taking advantage of the sign of double edges produced by second-order differential used for edge detection. Lip corners were extracted from the bottom half of the face by obtaining the highest contrast points from the contrast image. The algorithm was tested on the UNBC Pain Archive and achieved 100%, 100%, and 99.87% accuracy for eye centers, lip corners, and eyebrow corners, respectively. The algorithm is learning free, computational inexpensive, and can be modeled into embedded platforms.
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Bhattacherjee, P., Ramya, M.M. (2021). Accurate Detection of Facial Landmark Points Using Local Intensity Information for Emotion Detection. In: Zhang, YD., Senjyu, T., SO–IN, C., Joshi, A. (eds) Smart Trends in Computing and Communications: Proceedings of SmartCom 2020. Smart Innovation, Systems and Technologies, vol 182. Springer, Singapore. https://doi.org/10.1007/978-981-15-5224-3_1
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DOI: https://doi.org/10.1007/978-981-15-5224-3_1
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