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Essay on A New Algorithm for Age Group Recognition from Frontal Face Image

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In this paper, a new algorithm for age group recognition from frontal face image is presented. The algorithm classifies subjects into four different age categories in four key stages: Pre-processing, Facial feature extraction by a new projection method, Face feature analysis, and Age classification. In order to apply the algorithm to the problem, a face image database focusing on people’s age information is required. Because there are no such these databases, we created a database for this purpose, which is called Iranian Face Database (IFDB). IFDB contains digital images of people between 1 and 85 years old. After pre-processing, primary features of the faces in the database are accurately detected. The rest of the stages employ a neural network to classify the face into age groups using computed facial feature ratios and wrinkle densities. The experimental result shows that the algorithm identifies the age group with accuracy of 86.64%.
Keywords: Age Group Recognition; Facial Feature Extraction; Wrinkle Analysis; Artificial Neural Network; Face Image Database.
1. Introduction
Facial image processing is a research context, which has been studied by many researchers in recent decades. Face Recognition [1, 18], Facial Expression [9], Gender Classification [4], Face Detection [27], and Facial Feature Extraction [] are results of research in this area. Age group classification from facial images is one of the applications despite the theoretical and practical importance has been not considered adequately. This shortcoming is due to three reasons include (I) increasing in the number of classes, (II) inaccuracy even by human evaluations, and (III) non existence of a proper large data set.
Age related research have been considered...


... middle of paper ...


... be discussed in Section 2.6. VPF’, MIPF’ are formulated as follows:
Another contribution of this study is utilizing Principal Component Analysis (PCA) as a feature localizer. PCA involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
Usually PCA applies to a homogenous data set. In this paper, we assume that an image, itself, composed of a data collection which lies together in different rows. Therefore, PCA is applied on an image and the first and second principal components are extracted. Analysis of the extracted components helps locate facial features precisely.


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