Fingerprint-Based Gender Classification.

Conference Paper · January 2006with866 Reads
Source: DBLP
Conference: Proceedings of the 2006 International Conference on Image Processing, Computer Vision, & Pattern Recognition, Las Vegas, Nevada, USA, June 26-29, 2006, Volume 1

Gender classification from fingerprints is an important step in forensic anthropology in order to identify the gender of a criminal and minimize the list of suspects search. A dataset of 10-fingerprint images for 2200 persons of different ages and gender (1100 males and 1100 females) was analyzed. Features extracted were; ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, ridge count asymmetry, and pattern type concordance. Fuzzy C- Means (FCM), Linear Discriminant Analysis (LDA), and Neural Network (NN) were used for the classification using the most dominant features. We obtained results of 80.39%, 86.5%, and 88.5% using FCM, LDA, and NN, respectively. Results of this analysis make this method a prime candidate to utilize in forensic anthropology for gender classification in order to minimize the suspects search list by getting a likelihood value for the criminal gender.

    • "Here U and V are left and right odd vectors respectively, D is the diagonal matrix of particular values. SVD perturbation [13]uses these singular values to make the derived image (J). "
    Preview · Article · Apr 2016
    • "Gupta and Rao [91] used wavelet transformation and back propagation artificial neural networks to achieve an overall classification rate of 91.45% on a private database of 550 fingerprints (275 male, 275 female). Similar results were obtained by Badawi et al. [12], who employed Fuzzy Cognitive Maps (FCM) and neural networks to achieve a fingerprintbased gender classification rate of 88% (seeFigure 4 ). Additionally Tom et al. [255] used 2D wavelet transform and PCA to obtain 70% accuracy on a 547 subject-database, while Gnanasivam and Muttan [77] fused fingerprint features obtained by discrete wavelet transform (DWT) and singular value decomposition (SVD) to achieve an overall classification rate of 87.52%. "
    [Show abstract] [Hide abstract] ABSTRACT: Recent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., " young Asian female with dark eyes and brown hair "). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.
    Full-text · Article · Sep 2015 · IEEE Transactions on Information Forensics and Security
    • "Fingerprints have been used as vital parts for gender classification because of their unique properties. They are also important factors in forensic anthropology used to identify the gender [40]. A previous study [54] analyzed different features from fingerprint that are significantly different between males and females, where the highest gender classification rate was 88% using neural networks classifier and 86.5% using LDA classifier, respectively. "
    [Show description] [Hide description] DESCRIPTION: Gender contains a wide range of information regarding to the characteristics difference between male and female. Successful gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviors. First, this paper introduces the challenge and application for gender classification research. Then, the development and framework of gender classification are described. Besides, we compare these state-of- the-art approaches, including vision-based methods, biological information-based method, and social network information-based method, to provide a comprehensive review in the area of gender classification. In mean time, we highlight the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for the future work.
    No preview · Research · Jul 2015
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