International Journal of Biometrics (9 papers in press)
Incremental Robust Principal Component Analysis for Face Recognition Using Ridge Regression
by Haïfa Nakouri, Mohamed Limam
Abstract: Face recognition efficiency is extremely challenged by image corruption, noise, shadowing and variant face expressions. In this paper, we propose a reliable incremental face recognition algorithm to address this problem. The algorithm is robust to face image misalignment, occlusion, corruption and different style variations. To apply this for large face data streams, the proposed algorithm uses incremental robust principal component analysis (PCA) to regain the intrinsic data of a bunch of images regarding one subject. A novel similarity metric is defined for face recognition and classification. Five different databases and a base of four different criteria are used in the experiments to illustrate the reliability of the proposed method. Experiments point that it outperforms other existing incremental PCA approaches namely incremental singular value decomposition, add block singular value decomposition and candid covariance-free incremental PCA in terms of recognition rate under occlusions, facial expressions and image perspectives.
Keywords: Image alignment; Robust Principal Component Analysis; Incremental RPCA; Ridge regression.
An Integrated Framework for Evaluating the Performance of Age Progression Algorithms
by Andreas Lanitis, Νicolas Tsapatsoulis
Abstract: Facial age progression can play an important role in biometric authentication as it enables the long term person identification based on age progressed facial renderings. In this paper a systematic evaluation approach that can be used for assessing the performance of age progression algorithms is presented. The proposed method relies on the use of a dedicated dataset in conjunction with the development and use of machine-based performance evaluation metrics that allow the assessment of the intensity of aging effects added on a face along with an assessment of the identity preservation in age progressed images. The proposed performance evaluation framework can form the basis of implementing comprehensive comparative performance evaluation between different age progression methodologies, allowing in that way the evolution of the best algorithms.
Keywords: Age Progression; Facial Aging; Performance Evaluation.
An approach to matching fingerprints using cryptographic one-way hashes
by Qinghai Gao
Abstract: Password-based authentication systems match passwords in the formats of cryptographic one-way hashes without storing the original passwords. The method prevents someone with a copy of the password database from reversing the hashes and hacking into individual accounts easily. Biometric data cant be reproduced with 100% accuracy. Therefore, this technique has yet to be utilized directly to secure biometric data. Instead, biometric authentication system manages biometric data in two ways. The first is to store original biometric templates in encrypted formats. Real-time decryption is required upon authentication during which decrypted data can be stolen and performance could also be an issue for identification with a large database. The second is to store transformed templates obtained by transforming original templates with a similarity-preserving mechanism such that matching can be done in transformed domain. However, the requirement of similarity-preserving transformation makes the transformed templates vulnerable to reverse engineering. In this paper we propose a novel approach of transforming fingerprint templates which includes generation of random synthetic minutiae, projection of real minutiae to synthetic minutiae, and hashing of individual minutiae. The projection process can eliminate the intra-class variations of real minutiae. Matching is conducted between two templates only containing cryptographic one-way hashes. Inter-class variations are maintained with user-specific synthetic templates. This approach makes it possible to store and match fingerprint minutiae templates in the formats of cryptographic one-way hashes. Our testing results indicate its feasibility.
Keywords: Fingerprint; synthetic template; randomized minutiae tessellation; minutiae hashing; MD5 entry matching.
Immunological classifiers for accelerometer based gait identification
by Ismahene Dehache, Labiba Souici-Meslati
Abstract: Research in the field of biometrics is currently oriented towards behavioral modalities. The present study is interested in gait biometrics which occupies an important place due to its various advantages compared with other biometrics. This work suggests an identification system based on gait, using an accelerometer that allows the measurement of acceleration. The proposed approach for recognition is the immunological approach. Three types of classifiers; namely AIRS1, AIRS2 and AIRS parallel are suggested. An improvement on the three classifiers is done specially on the calculation of the affinity by integrating three types of distances: Hamming, Manhattan and Chebychev. The results are very satisfactory emphasizing the importance of gait modality and the interest of using immunological approaches in the domain of recognition of persons through behavioral biometrics.
Keywords: Gait identification; Artificial Immune Recognition System; biometrics; accelerometers; metrics.
Contact Lens Detection for Iris Spoofing Countermeasure
by Edward Tan, Anto Satriyo Nugroho, Maulahikmah Galinium
Abstract: The development of biometric authentication system should be followed by strengthening to spoofing attempts. Among various identifiers, iris has aroused many attentions due to its uniqueness and stability. Nevertheless, the use of iris for biometric authentication is accompanied by spoofing risk, for example using contact lens. In order to handle the spoofing attempts, its detection is inevitable part of a recognition system, to reduce the risk of forging system. Cosmetic contact lens is one of common spoofing material which is hard to be detected. In this study, weighted Local Binary Pattern (w-LBP) and simplified Scale Invariant Feature Transform (SIFT) descriptor were used to extract the feature of the iris, in which segmented using gradient magnitude and Fourier descriptor. Simplified SIFT descriptor is extracted at each pixel of iris image and being used to rank the Local Binary Pattern (LBP) sequence of encoding. The features were then presented to Support Vector Machine (SVM) classifier, for positive vs negative classification. Positive class means that contact lens was used by a person, and vice versa. The experimental results showed that combining SIFT and w-LBP as features for SVM yielded an accuracy of 84%.
Keywords: Biometrics; Iris Recognition; Contact Lens; Detection; Classification; Iris.
Fuzzy Similarity Based Classification method for gender recognition using 3D Facial Images
by Soufiane Ezghari, Naouar Belghini, Azeddine Zahi, Arsalane Zarghili
Abstract: In this paper, we propose a new fuzzy similarity based classification (FSBC) method for the task of gender recognition. The proposed method characterizes each individual by extracting geometrical features from a 3D facial Image using pertinent radial curves. Our approach includes representing the extracted features using fuzzy sets to handle imprecision in its values. Also the proposed FSBC method recognizes the gender of a new person by evaluating his similarity to the male and female samples pre-set as gender representatives set, then we aggregate the obtained similarities to compute the scores of belonging to each gender. In the end, we ascribe to each new person the gender with the higher score. With the proposed method, two main advantages are obtained: First, we used the OWA operator and RIM quantifier to define the percentage of significant features for the similarity assessment. Second, the aggregation process was performed using compensatory operators to ensure the selected gender has high similarities. Experiments were conducted using FRAV3D data base, by considering only one frontal pose in the gender representatives set. The obtained gender recognition rate of the proposed method was very promising compared to other classification method.
Keywords: Gender recognition; 3D facial Images; fuzzy similarity based classification (FSBC); OWA operator; RIM quantifier; compensatory operators.
Miscellaneous expertise of 3D facial landmarks in recent literature
by Federica Marcolin
Abstract: As the interest in human face grows, the attitude in formalizing and mapping it increases and improves as well. Facial landmarks, i.e. typical points of the face, are perfectly suited to the purpose, as their position on visage shape allows to build up a map of each human beings appearance. This turns to be extremely useful for a large variety of fields and related applications. Multipurpose medical is evidently leading in this sense, but other more uncommon ones, such as skull study for crime scenes, sex estimation, and attractiveness quantification, or more generic, such as morphological and cephalometric analyses, are present. Landmarks may be soft-tissue, laying on the skin, or hard-tissue, on bones and skull, but are anyway biometry-based and scientifically worldwide defined and approved, besides naturally being daily used by some communities such as maxillo-facial surgeons one. Also, other issues related to landmarks are here reported. One is the way the points are extrapolated from faces, meaning automatic vs manual annotation; the other one concerns misalignment between soft- and hard-tissue ones, both in terms of definition and allocation. Finally, a Cluster Analysis of the examined papers is performed depending on scope, landmarking method, and facial database features. The purpose is to face these topics by providing the reader with a comprehensive view of what 3D facial landmarks are and what they have been up to in 2014 and 2015. The aim is to offer to users the very up-to-date scenario, the best outcomes, i.e. the latest frontier of landmarks' talents and skills. The third dimension has a key role in this research, as it allows us to select the most prominent contributions, especially in terms of scientific advance innovativeness.
Keywords: Landmarks; soft-tissue landmark; hard-tissue landmark; fiducial point; 3D face; cluster analysis.
Optimal Feature Set Selection in Online Signature Verification
by Anuj Sharma, Sudhir Rohilla, R.K. Singla
Abstract: The online signature verification has attracted many researchers in recent past as it offers useful real life applications. This paper presents role of four types of feature sets as static, kinematics, structural and statistical in nature and these feature sets are analyzed in context of online signature verification. The signatures are verified as single trajectory and in combination of multiple sub-trajectories. We have applied feature sets with all possible permutations to signature trajectory and sub-trajectories. We have computed total eighty features and categorized to four feature sets on the basis of their behavioral characteristics. The inter-valued symbolic representation technique has been used to clearly understand the impact of each individual feature set or in combinations of feature set. The simulation results are presented using popular benchmark dataset SVC 2004 where both sub-datasets as TASK1 and TASK2 are used. The experimental results show that it is a promising correlation between different feature sets and suggest the optimal combination among several combinations of feature sets.
Keywords: online signature verification; inter-valued symbolic technique; static features; kinematic features; structural features; statistical features.
Persons Discriminating Visual Features for Recognizing Gender: LASSO Regression Model and Feature Analysis
by Samiul Azam, Marina Gavrilova
Abstract: Gender is one of the demographic attributes of a person, which is considered as a soft trait in the area of biometric. Several studies have been conducted to extract gender information based on a persons face image, gait pattern, fingerprint, iris, speech, and hand geometry. In this paper, we concentrate on predicting gender using a persons image aesthetic, which has never been studied before. We propose a visual preference model for discriminating males from females using LASSO regression. The preference model uses 57 dimensional feature vector containing 14 different perceptual image features. The model is evaluated on a database of 34000 images from 170 Flickr users (110 males and 60 females). Results show that maximum and average accuracy of predicting gender are around 91.67% and 84.38%, respectively, on 100 random sampling of training and testing datasets. The proposed method outperforms all existing state-of-the-art methods. In this paper, we also address two important research questions: which features are impacting the discrimination of male-female visual preferences, and how many images are sufficient for predicting a persons gender.
Keywords: Soft Biometrics; Social Biometrics; Gender Recognition; Image Aesthetics; Regression Model.