Automatic Detection of Facial Landmarks in Images with Different Sources of Variations
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Accurate and robust extraction of feature points in 2D facial images has multiple applications in biometric face recognition, facial expression classification, facial animation or human-computer interaction, among others. This paper describes a methodology for the fully automatic identification of 20 relevant facial points on static gray level images containing different types of variations. To solve the problem considered, we mainly use the shape and texture information provided by the images. The main advantage of this approach is its precision at point location, even for images with pronounced expressions. The presented method is tolerant to moderate scale changes and pose variations, and also to different illumination conditions. Our approach was tested on two of the most common databases used for facial expression analysis: Cohn-Kanade and JAFFE datasets, achieving respective average correct point detection rates of 94.2% and 96.15% on them. Our results were also compared to other related results presented in the literature on the same databases.
KeywordsFacial landmark detection face region location facial expressions anthropometric measurements pattern recognition
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