Conference Paper

Fingerprint-Based Gender Classification.

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


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.

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    • "With the non-appearance approach, features are extracted from biological information and social network-based information. Biological information comes from biometrics, e.g., voice [39], iris [16], fingerprint [40], emotional speech [41], and bio-signals, e.g., EEG [42], [43], ECG [27] and DNA [44] information. The Bio-signal refers to the biometric information regarding the human, which is used for gender classification. "
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    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.
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    • "Gender classification from fingerprints could be helpful in forensic anthropology in order to infer the gender of a criminal and minimize the list of suspects sought [43]. Although relatively few studies have empirically assessed gender differences in epidermal ridge thickness in human populations, they have pointed out that might be a useful tool in inferring gender from latent prints of unknown origin. "
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    ABSTRACT: Despite the fact that variation in ridge breadth is of biological, medical, and genetic interest, it has not received as much attention as other dermatoglyphic characteristics. Recently, sex differences in mean epidermal ridge breadth have been proposed in the field of forensic identification in order to infer gender from fingerprints found at the scene of a crime left by an unknown donor. The aim of this research was to analyze sexual, bimanual, and topological variations in epidermal ridge breadth on palmprints taken from a Spanish population sample for subsequent application in inferring gender from the palm marks. The material used in the present study was obtained from the palmprints of 200 individuals (100 males and 100 females) from the Caucasian Spanish. Since ridge breadth varies according to age, subjects of similar ages were recruited to ensure that growth had finished. Therefore, in order to assess topological variation in ridge density or number of ridges in a given space, the count was carried out for the five palmar areas: hypothenar, thenar/first interdigital, second interdigital, third interdigital, and fourth interdigital. This allowed the segmentation of 2000 ridge count areas for analysis. For this, two methods were used, one described by Cummins et al. (the ridge count was carried out along a 1cm line) and the other by Acree (the number of ridges per 25mm(2) of surface area). The results obtained by the second method can be compared with those obtained for the ten fingers from this same sample and evaluated in a previous study. The results have demonstrated the existence of topological differences in ridge thickness on the epidermal palm surface; also females present a significantly higher ridge density than men and, therefore, have narrower ridges over the entire palmar surface. Those sexual differences found in the sample population can be used for inferring the gender from palm marks left by an unknown donor. The hypotheses that could explain the variability in ridge breadth are evaluated according to the obtained results.
    Forensic science international 04/2013; 229(1). DOI:10.1016/j.forsciint.2013.03.014 · 2.14 Impact Factor
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    • "Gender estimation has been extensively studied, based on various kinds of human biometric features [1], such as face [2] [3], body [4], fingerprint [5] [6], hand shape [7], foot shape [8], and teeth [9], etc. Researchers in computer vision field usually seek gender hints from human face while physiologists and crime experts mainly tackle gender estimation problem through physiological features. "
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    ABSTRACT: We propose to estimate human gender from corresponding fingerprint and face information with the Bayesian hierarchical model. Different from previous works on fingerprint based gender estimation with specially designed features, our method extends to use general local image features. Furthermore, a novel word representation called latent word is designed to work with the Bayesian hierarchical model. The feature representation is embedded to our multimodality model, within which the information from fingerprint and face is fused at the decision level for gender estimation. Experiments on our internal database show the promising performance.
    Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on; 04/2010
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