Special Issue Published in International Journal of Trend in Research and Development (IJTRD), ISSN: 2394-9333, www.ijtrd.com International Conference on Intelligent Computing Techniques (ICICT -17) organized by Department of Computer Applications, Sarah Tucker College (Autonomous), Tirunelveli, TamilNadu on 1 st September 2017 37 | Page An Intelligent Approach for Age Detection Using Finger Prints N. Subathra, MCA,M.Phil Assistant Professor,Department of Computer Science Sarah Tucker College (Autonomous) Tirunelveli, Tamilnadu, India J. Stella Janci Rani, MCA,M.Phil Assistant Professor, Department of Computer Science Sarah Tucker College (Autonomous) Tirunelveli, Tamilnadu, India Abstract -The forensic investigators always search for fingerprint evidence which is seen as one of the best types of physical evidence linking a suspect to the crime. Discrete Wavelet Transform (DWT), the Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) has been used to estimate a person‟s age using his/her fingerprint. Mostly K nearest neighbor (KNN) is used as a robust classifier. The evaluation of the system is carried on using internal database of male and female fingerprints. Tested fingerprint is grouped into any one of the following five groups: upto 12, 13 -19, 20 -25, 26- 35 and 36 and above. The sample database is taken with the value of both male and female. The objective of this paper is also to classify the right hand fingerprints and identify whether it belong to male or female and determine his/her age. Keywords- Gender Classification, Fingerprint, Discrete Wavelet Transform, Singular Value Decomposition, Principle Component Analysis, k nearest neighbor. I. INTRODUCTION Digital Image Processing refers to processing of digital images by using digital computers. Digital Image processing has various steps for processing the image and will perform Object Recognition. Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on its descriptors. Many human body features have been used to estimate sex/gender. Some of recent examples include foot print ratio, metatarsals, humerus, long bones of the arm, foot shape, femoral head, foot and shoe dimensions, patella, teeth and radial and ulnar bone lengths. Biometric identification systems are widely used for unique identification of humans mainly for verification and identification. Fingerprint has been used as a biometric for the gender and age identification because of its unique nature and do not change throughout the life of an individual [1]. Gender and Age information is important to provide investigative leads for finding unknown persons. Existing methods for gender classification have limited use for crime scene investigation. In this work, gender and age of a person is classified from the fingerprint using DWT and SVD and PCA. In fingerprint, the primary dermal ridges (ridge counts) are formed during the gestational weeks 12-19 and the resulting fingerprint ridge configuration (fingerprint) is fixed permanently [2-3]. The patterns of ridges on our finger pads are unique: no two individuals, including identical twins have fingerprints that are not same. Also, the variability of epidermal ridge breadth in humans is substantial [4]. Dermatoglyphic features statistically differ between the sexes, ethnic groups and age categories [5]. It is proved by various researchers; a fingerprint can be processed for the sex determination [6-11]. Figure 1 illustrates the process of DWT, SVD and PCA based gender classification system. Fig.1 DWT, SVD and PCA based gender classification system. Wavelet transform is a transform. Its provides the time frequency representation. In this work Discrete Wavelet transform used for gender classification. II. LITERATURE SURVEY Earlier work on gender classification based on the ridge density shows that the ridge density is greater for female than male [7, 8, 10,11] and [9] analyzed fingerprints of a tribal population of Andhra Pradesh (India) and showed the evident that the males showing higher mean ridge counts than females. A new approach for personal identification that utilizes simultaneously acquired finger vein and finger surface images is analyzed. This paper investigates two new score level combination approaches, i.e., holistic and nonlinear fusion, for combining finger vein and finger texture matching scores [12]. A new method for gender classification of fingerprint images based on levels using only DWT and SVD has been done [13]. The Fingerprint biometric is used to authenticate a person. Transform Domain Fingerprint Identification Based on DTCWT is proposed. The Fingerprint is preprocessed to a suitable size that suit DTCWT. The Fingerprint features are obtained by applying DTCWT with different levels.[14]. III. FINGERPRINT FEATURE EXTRACTION For any pattern recognition, Feature extraction is fundamentally needed. For Feature extraction we have used the techniques of DWT, SVD and PCA. These techniques are discussed below. A. DWT based Feature Extraction DWT is a linear transformation that operates on a data vector whose length is an integer power of two, transforming it into a numerically different vector of the same length. It is a tool that separate data into different frequency components and the studies each component with resolution matched to its scale. 2D wavelet transform, decompose the image into 4 subbands. That is Low – low, Low – High, High – low, High- High. Most of the