A face clustering method based on facial shape information

2011 ◽  
Author(s):  
Shu-Cong Zhang ◽  
Bin Fang ◽  
Ying-Zong Liang ◽  
Jing Wen ◽  
Lei Wu
Keyword(s):  
Clustering Method ◽  
Shape Information ◽  
Face Clustering ◽  
Facial Shape

Gabor-based Coding and Facial Similarity Perception

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 250-250
Author(s):  
M J Lyons ◽  
K Morikawa ◽  
S Akamatsu
Keyword(s):  
Human Subjects ◽  
Receptive Fields ◽  
Shape Information ◽  
Race Effect ◽  
Facial Shape ◽  
Cortex Area ◽  
The Face ◽  
Similarity Ratings ◽  
Facial Similarity ◽  
Facial Representation

Little is known about how facial representation in the face-selective areas of inferotemporal cortex is built up from the output of earlier visual areas such as primary visual cortex (area V1). We report work on a model of perceived facial similarity based on topographically ordered aggregates of localised, oriented, and spatial-frequency-selective receptive fields characteristic of V1 cells. The receptive fields are approximated with a set of Gabor filters. This Gabor-based code allows representation of the fine differences in texture and configuration needed for facial discrimination processes. Lyons and Morikawa (1996 Investigative Ophthalmology and Visual Science37 910), showed that Gabor-based similarity is a good predictor of facial similarity for comparisons of fairly similar faces but not sufficient to model experience-dependent effects such as the ‘other race effect’. Here we report results of a study on the effects of image negation on facial similarity perception. Negation of image gray levels interferes with face recognition (Bruce and Lanton, 1994 Perception23 803 – 822) while preserving 2-D facial-shape information. The Gabor similarity measure models non-endstopped complex cells of V1 and is not affected by image negation. One group of subjects judged similarity among a set of normal gray-scale facial images while another group judged similarity between negative images of the same stimuli. Agreement between the model and human subjects did not decrease with image negation. Moreover, human similarity ratings between negative faces were strongly correlated with those between positives. These results support Gabor-based similarity as a model for facial similarity perception.

scholarly journals A Spatial Shape Constrained Clustering Method for Mammographic Mass Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jian-Yong Lou ◽  
Xu-Lei Yang ◽  
Ai-Ze Cao
Keyword(s):  
Shape Function ◽  
Deterministic Annealing ◽  
Constrained Clustering ◽  
Clustering Method ◽  
Shape Information ◽  
Intensity Information ◽  
Average Probability ◽  
Mass Segmentation ◽  
Spatial Shape ◽  
Similar Intensity

A novel clustering method is proposed for mammographic mass segmentation on extracted regions of interest (ROIs) by using deterministic annealing incorporating circular shape function (DACF). The objective function reported in this study uses both intensity and spatial shape information, and the dominant dissimilarity measure is controlled by two weighting parameters. As a result, pixels having similar intensity information but located in different regions can be differentiated. Experimental results shows that, by using DACF, the mass segmentation results in digitized mammograms are improved with optimal mass boundaries, less number of noisy patches, and computational efficiency. An average probability of segmentation error of 7.18% for well-defined masses (or 8.06% for ill-defined masses) was obtained by using DACF on MiniMIAS database, with 5.86% (or 5.55%) and 6.14% (or 5.27%) improvements as compared to the standard DA and fuzzyc-means methods.

FUZZY ART-BASED IMAGE CLUSTERING METHOD FOR CONTENT-BASED IMAGE RETRIEVAL

2007 ◽  
Vol 06 (02) ◽  
pp. 213-233 ◽  
Author(s):  
SANG-SUNG PARK ◽  
KWANG-KYU SEO ◽  
DONG-SIK JANG
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Image Clustering ◽  
Clustering Method ◽  
Image Region ◽  
Shape Information ◽  
Fuzzy Art ◽  
The Neural Network ◽  
Joint Histogram ◽  
Noise Input

In this paper, an image clustering method that is essential for content-based image retrieval in large image databases efficiently is proposed by color, texture, and shape contents. The dominant triple HSV (Hue, Saturation, and Value), which are extracted from quantized HSV joint histogram in the image region, are used for representing color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Due to its algorithmic simplicity and the several merits that facilitate the implementation of the neural network, Fuzzy ART has been exploited for image clustering. Original Fuzzy ART suffers unnecessary increase of the number of output neurons when the noise input is presented. Therefore, the improved Fuzzy ART algorithm is proposed to resolve the problem by differently updating the committed node and uncommitted node, and checking the vigilance test again. To show the validity of the proposed algorithm, experimental results on image clustering performance and comparison with original Fuzzy ART are presented in terms of recall rates.

scholarly journals Face Song Player According to Facial Expressions

2016 ◽  
Vol 6 (6) ◽  
pp. 2805
Author(s):  
Samule Lee ◽  
Seong-Yoon Shin
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Daily Life ◽  
Low Cost ◽  
Substantial Effect ◽  
Shape Information ◽  
Facial Contour ◽  
Contour Lines ◽  
Facial Shape ◽  
Insufficient Time

<p>Contemporary people have highly insufficient time and means of relieving their stress. Provision of a program that can solve such stress in daily life would make one’s life substantially more enjoyable. In this thesis, Face Song Player, which is a system that recognizes the facial expression of an individual and plays music that is appropriate for such person, is presented. It studies information on the facial contour lines and extracts an average, and acquires the facial shape information. MUCT DB was used as the DB for learning. For the recognition of facial expression, an algorithm was designed by using the differences in the characteristics of each of the expressions on the basis of expressionless images. Facial expression is extracted by acquiring information on the eyes, eyebrows, eyelids, mouth, lips and nasal cheeks for expressions of happiness, surprise and sorrow as well as absence of expression. There is an advantage of being able to obtain a substantial effect with very low cost through this system.</p>

Face Song Player According to Facial Expressions

2016 ◽  
Vol 6 (6) ◽  
pp. 2805
Author(s):  
Samule Lee ◽  
Seong-Yoon Shin
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Daily Life ◽  
Low Cost ◽  
Substantial Effect ◽  
Shape Information ◽  
Facial Contour ◽  
Contour Lines ◽  
Facial Shape ◽  
Insufficient Time

<p>Contemporary people have highly insufficient time and means of relieving their stress. Provision of a program that can solve such stress in daily life would make one’s life substantially more enjoyable. In this thesis, Face Song Player, which is a system that recognizes the facial expression of an individual and plays music that is appropriate for such person, is presented. It studies information on the facial contour lines and extracts an average, and acquires the facial shape information. MUCT DB was used as the DB for learning. For the recognition of facial expression, an algorithm was designed by using the differences in the characteristics of each of the expressions on the basis of expressionless images. Facial expression is extracted by acquiring information on the eyes, eyebrows, eyelids, mouth, lips and nasal cheeks for expressions of happiness, surprise and sorrow as well as absence of expression. There is an advantage of being able to obtain a substantial effect with very low cost through this system.</p>

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