Soft Biometrics Research Papers - Academia.edu (original) (raw)

Classification of gender from fingerprints is one of the important steps in forensic anthropology. This forensic anthropology is used to identify the gender of a criminal in order to minimize the suspects list of search. A very few researcher have worked on gender classification using fingerprints and have gain the competitive results. In this work we are trying to fuse the fingerprint and age biometrics for gender classification. The real fingerprints were collected from different age groups such as 15-20 years and 20-60 years of the rural and urban people. According to this experimental observation soft biometric information can be used significantly to improve the recognition performance of biometric system. The overall performance of the proposed method is found to be satisfactory and more competitive.

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- Texture Analysis, Soft Biometrics, Frequency Domain, Gender classification

Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices.

- by Oscar Miguel-Hurtado
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- Biometrics, Soft Biometrics

The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems (COTS). All experiments are reported using the labeled faces in the wild (LFW) database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores. Index Terms-Soft biometrics, hard biometrics, commercial systems, unconstrained scenarios. I. INTRODUCTION S OFT biometrics refer to physical and behavioral traits that can be semantically described by humans [9], [19]. Although soft biometric traits may not possess sufficient distinctiveness or uniqueness to allow highly accurate recognition [4], they can be useful to enhance person recognition under certain conditions. For instance, Dantcheva et al. [10] suggested that it might be possible to perform recognition when considering a sufficient number of them, in a bag of

- by Fernando Alonso-Fernandez
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- Engineering, Computer Science, Artificial Intelligence, Biometrics

Recognition of newborn after birth is a critical issue for hospitals, maternity word and other places where multiple birthstake place. Switching and abduction of newborn is a global challenge and research done to meet this challenge is minimal. Most of the biometric systems developed are for adults and very few of them address the issue of newborn identification.The ear of newborn is a perfect source of data for passive identification of newborn as they are the highly non cooperative users of biometrics. The four important characteristics of ear biometrics: universality, uniqueness, permanence and collectability make it a very potential biometric trait for the identification of newborn. Further the use of soft-biometric data like gender, blood group, height and weight along with ear enhances the accuracy for identification of newborn. The objective of this paper is to demonstrate the concept of fusing earand soft-biometrics for recognition of newborn. The main contributions of the research are (a) Design and implementation for fusion of ear and soft biometric for recognition of 210 newborn. (b)Preparation of ear and soft-biometric database of newborn. Fusion of ear and soft-biometrics results in an improvement of approximately 5.59% over the primary biometric system i.e. ear.

- by Signal & Image Processing : An International Journal (SIPIJ) - WJCI Indexed and +1
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- Recognition, Fusion, Newborn, Ear

Face recognition indeed plays a major rule in the biometrics security environment. Facial marks as for example freckles, moles, scars etc that are soft biometric traits have played a crucial role in identifying the human face. To provide secure authentication, we require robust methodology for recognizing and authentication of the human face. However, there are numbers of difficulties in recognizing the human face and authentication of the person perfectly. The difficulty includes low quality of images due to sparse dark or light disturbances. To overcome such kind of problems, powerful algorithms are required to filter the images and detect the face and facial marks. This technique comprise extensively of detecting the different facial marks from that of low quality images which have salt and pepper noise in them. Initially we applied (AMF) Adaptive Median Filter to filter the images. The filtered images are then extracted to detect the primary facial feature using a powerful algor...

- by Dr K M Mehata
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- Active Shape Model, Soft Biometrics, ADAPTIVE MEDIAN FILTER

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

- by Sarah Stevenage
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- Demography, Algorithms, Machine Learning, Biometrics

Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces and CAS-PEALR1.

- by International Journal of Electrical and Computer Engineering (IJECE) and +1
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- CNN, Deep Learning, Gender Recognition, Soft Biometrics

In face recognition technology, facial marks identification method is one of the unique facial identification tasks using soft biometrics. Also facial marks information can enhance the face matching score to improve the face recognition performance. As numbers of folk apply their face with cosmetic items, some of the facial marks are invisible or hidden from their faces. In the literature, they used AAM (Active Appearance Model) and LoG (Laplacian of Gaussian) method to detect the facial marks. However, to the best of our knowledge, the methods related to the detection of facial marks are poor in performance in cosmetic applied faces. In this paper, we propose robust method to detect the facial marks such as tattoos, scars, freckles and moles etc. Initially we apply active appearance model (AAM) for facial feature detection purpose. In addition to this prior model we apply Canny edge detector method to detect the facial mark edges. Finally SURF is used to detect the hidden facial ma...

- by Dr K M Mehata
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- Surf, Soft Biometrics, Canny edge detection

A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft biometrics from inertial sensor recordings at a single body location from a single step. We recorded accelerations and angular velocities of 26 subjects using integrated measurement units (IMUs) attached at four locations (chest, lower back, right wrist and left ankle) when performing standardized gait tasks. The collected data were segmented into individual walking steps. We trained random forest classifiers in order to estimate soft biometrics (gender, age and height). We applied two different validation methods to the process, 10-fold cross-validation and subject-wise cross-validation. For all three classification tasks, we achieve high accuracy values for all four sensor locations. From these results, we can conclude that the data of a single walking step (6D: accelerations and angular velocities) allow for a robust estimation of the gender, height and age of a person. Keywords: estimation of soft biometrics; gender, age and height estimation from inertial data; gait analysis; inertial sensors to estimate gender, age and height; accelerometers

- by Björn Krüger and +1
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- Machine Learning, Accelerometers, Soft Biometrics

This paper presents a study on personal aesthetics, a recent soft biometrics application where the goal is to recognize people by considering the images they like. Here we propose a multi-level approach, where each level is intended as a low-dimensional space where the images preferred by a user can be projected, and similar images are mapped nearby, namely a Counting Grid. Multiple levels are generated by adopting Counting Grids at different resolutions, corresponding to analyze images at different grains. Each level is then associated to an exemplar Support Vector Machine, which separates the images of an individual from the rest of the users. Putting together multiple levels gives a battery of classifiers whose performances are very good: on a dataset of 200 users, and 40K images, using 5 preferred images as biometric template gives 97% of probability of guessing the correct user; as for the verification capability, the equal error rate is 0.11. The approach has also been tested with diverse comparative methods and different features, showing that color image properties are crucial to encode the personal aesthetics, and that high-level information (as the objects within the images) could be very effective, but current methods are not robust enough to catch it.

- by Cristina Segalin
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- Biometrics, Biometrics And Identity, Computational Aesthetics, Soft Biometrics