DETECTION OF FACES AND FACIAL FEATURES IN COLOR IMAGES

Author(s):  
ARCHANA H. SABLE ◽  
K. C. JONDHALE
Keyword(s):  
2021 ◽  
Author(s):  
Christopher M. Jernigan ◽  
Jay A Stafstrom ◽  
Natalie C Zaba ◽  
Caleb C Vogt ◽  
Michael J Sheehan

Visual individual recognition requires animals to distinguish among conspecifics based on appearance. Though visual individual recognition has been reported in a range of taxa, the features that animals rely on to discriminate between individuals are often not well understood. Northern paper wasp females, Polistes fuscatus, possess individually distinctive color patterns on their faces, which mediate individual recognition. It is currently unclear what facial features P. fuscatus use to distinguish individuals. The anterior optic tubercle, a chromatic processing brain region, is especially sensitive to social experience during development, suggesting that color may be important for recognition in this species. We sought to test the roles of color in wasp facial recognition. Color may be important simply because it creates a pattern. If this is the case, then wasps should perform similarly when discriminating color or grayscale images of the same faces. Alternatively, color itself may be important for recognition, which would predict poorer performance on grayscale image discrimination relative to color images. We found wasps trained on grayscale faces, unlike those trained on color images, did not perform better than chance. Suggesting that color is necessary for the recognition of an image as a face by the wasp visual system.


Author(s):  
FRANK Y. SHIH ◽  
SHOUXIAN CHENG ◽  
CHAO-FA CHUANG ◽  
PATRICK S. P. WANG

In this paper, we present image processing and pattern recognition techniques to extract human faces and facial features from color images. First, we segment a color image into skin and non-skin regions by a Gaussian skin-color model. Then, we apply mathematical morphology and region filling techniques for noise removal and hole filling. We determine whether a skin region is a face candidate by its size and shape. Principle component analysis (PCA) is used to verify face candidates. We create an ellipse model to locate eyes and mouths areas roughly, and apply the support vector machine (SVM) to classify them. Finally, we develop knowledge rules to verify eyes. Experimental results show that our algorithm achieves the accuracy rate of 96.7% in face detection and 90.0% in facial feature extraction.


Author(s):  
Jin Ok Kim ◽  
Jin Soo Kim ◽  
Young Ro Seo ◽  
Bum Ro Lee ◽  
Chin Hyun Chung ◽  
...  
Keyword(s):  

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