scholarly journals Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU)

2015 ◽  
Vol 45 ◽  
pp. 215-225 ◽  
Author(s):  
Ch. Rajendra Babu ◽  
E. Sreenivasa Reddy ◽  
B. Prabhakara Rao
Keyword(s):  
Group Classification ◽  
Age Group ◽  
Age Group Classification ◽  
Facial Images

scholarly journals Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU) and Fuzzy Texture

2019 ◽  
pp. 605-611
Author(s):  
Jammula Nimitha ◽  
Kuraganti Nukeswari ◽  
Kuraganti Sudha ◽  
Kurapati Sumithra ◽  
Rama Devi Gunnam
Keyword(s):  
Age Groups ◽  
Group Classification ◽  
Facial Features ◽  
Age Group ◽  
Human Beings ◽  
Age Group Classification ◽  
Facial Images

We as human beings can estimate the age of a person based on his facial features but there are situations where there is a need for the computers to determine the age of a person based on the picture or photograph. Here comes the situation to teach a machine to determine the age group of a person with his picture. This is applicable in the fields like determining the age of a criminal with his picture or determining the age of a patient when he has undergone an accident and many other fields. To address this problem the paper proposed a technique of finding the age with Rank Based Edge Texture Unit (RETU). The uniqueness of this method is that it divides the age group into 7 classes i.e. the age groups are 1-10, 11-20, 21-30,31-0,41-50,51-60,>60 . With this method, the results cope up to 97.16% and to slightly increase the efficiency the present paper proposes to add Fuzzy Texton features.

Age-Group Classification of Male Alcoholics as a Function of MMPI Experimental Scales

1984 ◽  
Vol 55 (3) ◽  
pp. 697-698
Author(s):  
Michael P. Carey ◽  
Michael E. Faulstich ◽  
Joseph G. Delatte
Keyword(s):  
Discriminant Analysis ◽  
Group Classification ◽  
Chronological Age ◽  
Validity Scales ◽  
Age Group ◽  
Age Group Classification

91 male alcoholics were assigned to one of four groups according to chronological age. Discriminant analysis using 15 experimental scales as variables indicated that the percentage of correctly classified alcoholics was approximately equivalent with the results of an earlier study by Faulstich, et al. employing the 10 clinical and three validity scales as predictors. These findings are discussed in reference to directions for further research.

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