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This paper presents an alternative approach, where gait is collected by the sensors attached to the person's body. Such wearable sensors record motion (e.g. acceleration) of the body parts during walking. The recorded motion signals are then investigated for person recognition purposes. We analyzed acceleration signals from the foot, hip, pocket and arm. Applying various methods, the best EER obtained for foot-, pocket-, arm- and hip- based user authentication were 5%, 7%, 10% and 13%, respectively. Furthermore, we present the results of our analysis on security assessment of gait. Studying gait-based user authentication (in case of hip motion) under three attack scenarios, we revealed that a minimal effort mimicking does not help to improve the acceptance chances of impostors. However, impostors who know their closest person in the database or the genders of the users can be a threat to gait-based authentication. We also provide some new insights toward the uniqueness of gait in case of foot motion. In particular, we revealed the following: a sideway motion of the foot provides the most discrimination, compared to an up-down or forward-backward directions; and different segments of the gait cycle provide different level of discrimination.

The aim of this work is to carry out a comparative study of face recognition methods that are suitable to work in unconstrained environments. The analyzed methods are selected by considering their performance in former comparative studies, in addition to be real-time, to require just one image per person, and to be fully online. In the study two local-matching methods, histograms of LBP features and Gabor Jet descriptors, one holistic method, generalized PCA, and two image-matching methods, SIFT-based and ERCF-based, are analyzed. The methods are compared using the FERET, LFW, UCHFaceHRI, and FRGC databases, which allows evaluating them in real-world conditions that include variations in scale, pose, lighting, focus, resolution, facial expression, accessories, makeup, occlusions, background and photographic quality. Main conclusions of this study are: there is a large dependence of the methods on the amount of face and background information that is included in the face's images, and the performance of all methods decreases largely with outdoor-illumination. The analyzed methods are robust to inaccurate alignment, face occlusions, and variations in expressions, to a large degree. LBP-based methods are an excellent election if we need real-time operation as well as high recognition rates.

We present a novel online signature verification system based on the Fast Fourier Transform. The advantage of using the Fourier domain is the ability to compactly represent an online signature using a fixed number of coefficients. The fixed-length representation leads to fast matching algorithms and is essential in certain applications. The challenge on the other hand is to find the right preprocessing steps and matching algorithm for this representation. We report on the effectiveness of the proposed method, along with the effects of individual preprocessing and normalization steps, based on comprehensive tests over two public signature databases. We also propose to use the pen-up duration information in identifying forgeries. The best results obtained on the SUSIG-Visual subcorpus and the MCYT-100 database are 6.2% and 12.1% error rate on skilled forgeries, respectively. The fusion of the proposed system with our state-of-the-art Dynamic Time Warping (DTW) system lowers the error rate of the DTW system by up to about 25%. While the current error rates are higher than state-of-the-art results for these databases, as an approach using global features, the system possesses many advantages. Considering also the suggested improvements, the FFT system shows promise both as a stand-alone system and especially in combination with approaches that are based on local features.

Biometrics refer to identity verification of individuals based on some physiologic or behavioural characteristics. The typical authentication process of a person consists in extracting a biometric pattern of him/her and matching it with the stored pattern for the authorised user obtaining a similarity value between patterns. In this work an efficient method for persons authentication is showed. The biometric pattern of the system is a set of feature points representing landmarks in the retinal vessel tree. The pattern extraction and matching is described. Also, a deep analysis of similarity metrics performance is presented for the biometric system. A database with samples of retina images from users on different moments of time is used, thus simulating a hard and real environment of verification. Even in this scenario, the system allows to establish a wide confidence band for the metric threshold where no errors are obtained for training and test sets.

The electrocardiogram (ECG) is an emerging novel biometric for human identification. One challenge for the practical use of ECG as a biometric is minimizing the time needed to acquire user data. We present a methodology for identity verification that quantifies the minimum number of heartbeats required to authenticate an enrolled individual. The approach rests on the statistical theory of sequential procedures. The procedure extracts fiducial features from each heartbeat to compute the test statistics. Sampling of heartbeats continues until a decision is reached—either verifying that the acquired ECG matches the stored credentials of the individual or that the ECG clearly does not match the stored credentials for the declared identity. We present the mathematical formulation of the sequential procedure and illustrate the performance with measured data. The initial test was performed on a limited population, twenty-nine individuals. The sequential procedure arrives at the correct decision in fifteen heartbeats or fewer in all but one instance and in most cases the decision is reached with half as many heartbeats. Analysis of an additional 75 subjects measured under different conditions indicates similar performance. Issues of generalizing beyond the laboratory setting are discussed and several avenues for future investigation are identified.

The combination of cryptology and biometrics has emerged as promising component of information security. Despite the current popularity of palmprint biometric, there has not been any attempt to investigate its usage for the fuzzy vault. This paper therefore investigates the possible usage of palmprint in fuzzy vault to develop a user friendly and reliable crypto system. We suggest the use of both symmetric and asymmetric approach for the encryption. The ciphertext of any document is generated by symmetric cryptosystem; the symmetric key is then encrypted by asymmetric approach. Further, Reed and Solomon codes are used on the generated asymmetric key to provide some error tolerance while decryption. The experimental results from the proposed approach on the palmprint images suggest its possible usage in an automated palmprint-based key generation system.

Face recognition based on spatial features has been widely used for personal identity verification for security-related applications. Recently, near-infrared spectral reflectance properties of local facial regions have been shown to be sufficient discriminants for accurate face recognition. In this paper, we compare the performance of the spectral method with face recognition using the eigenface method on single-band images extracted from the same hyperspectral image set. We also consider methods that use multiple original and PCA-transformed bands. Lastly, an innovative spectral eigenface method which uses both spatial and spectral features is proposed to improve the quality of the spectral features and to reduce the expense of the computation. The algorithms are compared using a consistent framework.

This paper proposes a novel criterion for an improved writer enrolment based on an entropy measure for online genuine signatures. As online signature is a temporal signal, we measure the time-normalized entropy of each genuine signature, namely, its average entropy per second. Entropy is computed locally, on portions of a genuine signature, based on local density estimation by a Client-Hidden Markov Model. The average time-normalized entropy computed on a set of genuine signatures allows then categorizing writers in an unsupervised way, using a K-Means algorithm. Linearly separable and visually coherent classes of writers are obtained on MCYT-100 database and on a subset of BioSecure DS2 containing 104 persons (DS2-104). These categories can be analyzed in terms of variability and complexity measures that we have defined in this work. Moreover, as each category can be associated with a signature prototype inherited from the K-Means procedure, we can generalize the writer categorization process on the large subset DS2-382 from the same DS2 database, containing 382 persons. Performance assessment shows that one category of signatures is significantly more reliable in the recognition phase, and given the fact that our categorization can be used online, we propose a novel criterion for enhanced writer enrolment.

This paper describes a novel method for representing different facial expressions based on the shape space vector (SSV) of the statistical shape model (SSM) built from 3D facial data. The method relies only on the 3D shape, with texture information not being used in any part of the algorithm, that makes it inherently invariant to changes in the background, illumination, and to some extent viewing angle variations. To evaluate the proposed method, two comprehensive 3D facial data sets have been used for the testing. The experimental results show that the SSV not only controls the shape variations but also captures the expressive characteristic of the faces and can be used as a significant feature for facial expression recognition. Finally the paper suggests improvements of the SSV discriminatory characteristics by using 3D facial sequences rather than 3D stills.

This paper presents a novel approach for changeable and privacy preserving face recognition. We first introduce a new method of biometric matching using the sorted index numbers (SINs) of feature vectors. Since it is impossible to recover any of the exact values of the original features, the transformation from original features to the SIN vectors is noninvertible. To address the irrevocable nature of biometric signals whilst obtaining stronger privacy protection, a random projection-based method is employed in conjunction with the SIN approach to generate changeable and privacy preserving biometric templates. The effectiveness of the proposed method is demonstrated on a large generic data set, which contains images from several well-known face databases. Extensive experimentation shows that the proposed solution may improve the recognition accuracy.