International Journal of Biometrics (8 papers in press)
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 Sudhir Rohilla, Anuj Sharma, 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.
Classification and Gender Recognition from Veiled-Faces
by Ahmad B. Hassanat, V. B. Surya Prasath, Bassam M. Al-Mahadeen, Samaher Madallah Moslem Alhasanat
Abstract: This study aims to investigate to what extent a computer system can identify veiled-human and recognize gender using eyes and the uncovered part of the face. For the purpose of this study, we have created a new veiled persons image (VPI) database shot using a mobile phone camera, imaging 100 different veiled-persons over two sessions. After preprocessing and segmentation we used a fused method for feature extraction. The fusion occurs between geometrical (edge ratio) and textural (probability density function of the color moments) features. The experimental results using different classifiers were ranging from 88.63% to 97.22% for person identification accuracy before feature selection and up to 97.55% after feature selection. The proposed method achieved up to 99.41% success rate for gender classification.
Keywords: face recognition; biometrics; veiled faces; features fusion.
Signature Recognition Using Binary Features and KNN
by Hedjaz Hezil, Rafik Djemili, Houcine Bourouba
Abstract: This paper proposes the use of binary features in offline signature recognition systems. Indeed, offline signature recognition finds mainly its importance for the authentication of administrative and official documents in which a higher accuracy is needed. In the proposed approach, features are extracted by using two descriptors: Binary Statistical Image Features (BSIF) and Local Binary Patterns (LBP). To assess the reliability of the method, experiments were carried out using two publicly available datasets, MCYT-75 and GPDS-100 databases. Using a k-nearest neighbor classifier, recognition performances reach values high as 97.3% and 96.1% for MCYT-75 and GPDS-100 databases respectively.In signature verification, the classification accuracy measured with equal error rate (EER) achieved 4.2% and 4.8% respectively on GPDS-100 and GPDS-160. In addition, the EER for the MCYT-75 database has attained 7.78%. All those accuracies outperformed various performance results reported in literature.
Keywords: Offline signature recognition; feature extraction; biometric; LBP; BSIF; KNN.
Palmprint Identification and Verification with minimal number of features
by Hemantha Kumar Kalluri
Abstract: In this paper, palmprint verification and identification with minimum number of features is proposed. Apply the Wide Principal Line Extractors (WPLEs) on the Region of Interest (ROI) to generate Wide Principal Line Images (WPLIs). The WPLI is segmented into 2x2, 4x4, 8x8 and 16x16 and the feature value is extracted directly from each segment. Experiments are conducted by using the extracted features. The results show that the Equal Error Rate (EER), Decidability Index (DI) and Correct
Recognition Rate (CRR) of the proposed approach is better than existing methods for PolyUPalmprint Database.
Keywords: Palmprint; Biometrics; Region of Interest; Feature Extraction; PalmprintrnIdentification; Palmprint Verification.
GSI: Efficient Spatio-Temporal Template for Human Gait Recognition
by Mohammad H. Ghaeminia, Shahriar B. Shokouhi
Abstract: Human gait recognition is a challenging task in computer vision community. In order to represent the gait in video sequences, the most common feature is a gait template. Many efficient templates have been developed recently, however, the effectiveness of the proposed gait motion models is still under investigation. A novel template-based feature, named Gait Salient Image (GSI) is introduced in this paper. The main contribution of the proposed GSI template is encoding the motion energy of gait into a single template. This idea is being conceptualized by applying appropriate spatio-temporal filter for extracting motion features from the sequences and averaging it over a gait period. To show how GSI-based feature is being efficient, the proposed template is classified using PCA+LDA. Extensive experiments on popular gait databases reveal an improvement over the available methods in terms of efficiency and accuracy. The value of recognition rate is 58.44% for Rank1 and 76.60% for Rank5 based on the USF database.
Keywords: gait recognition; spatio-temporal filtering; template-based features.