Anthropometry of the head and face

Shape and geometry features are encoded from contour of the hand only.Robust preprocessing is introduced to cope with the noise and disjoint fingers.Hand orientation and finger registration is applied to provide more flexibility.Two level score fusion is adopted to enhance the verification performance.Promising results are obtained over contact and contactless (IITD) datasets. A multimodal biometric system for personal identity verification is proposed using hand shape and hand geometry in this paper. Shape and geometry features are derived with the help of only contour of the hand image for which only one image acquisition device is sufficient. All the processing is done with respect to a stable reference point at wrist line which is more stable as compared to the centroid against the finger rotation and peaks and valleys determination. Two shape based features are extracted by using the distance and orientation of each point of hand contour with respect to the reference point followed by wavelet decomposition to reduce the dimension. Seven distances are used to encode the geometrical information of the hand. Shape and geometry based features are fused at score levels and their performances are evaluated using standard ROC curves between false acceptance rate, true acceptance rate, equal error rate and decidability index. Different similarity measures are used to examine the accuracy of the introduced method. Performance of system is analyzed for shape based (distance and orientation) and geometrical features individually as well as for all possible combinations of feature and score level fusion. The proposed features and fusion methods are studied over two hand image datasets, (1) JUET contact database of 50 subjects having 10 templates each and (2) IITD contactless dataset of 240 subjects with 5 templates each. The proposed method outperforms other approaches with the best 0.31% of EER.

Identity verification using shape and geometry of human hands

Biometrics trait using finger-vein has attracted numerous attention from researchers all over the world since the last decade. Various approaches have been proposed in regard to improving the accuracy of identification result. This paper discusses on the approaches taken from other researches on preprocessing, feature extraction and classification stage specifically for recognizing individual identity. The strengths and weaknesses of these approaches are critically reviewed. The classification approach using machine learning method is highlighted to determine the future direction and to fill the research gap in this field.

A Review of Finger-Vein Biometrics Identification Approaches

A novel hand biometric authentication method based on measurements of the user’s stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information, associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password ‘iloveu’ in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, ‘i’ , ‘l’ , ‘o’ , ‘v’ , ‘e’ , and ‘u’. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. It is believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy of this novel biometric authentication model which shows up to 93.75% recognition accuracy.

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A biometric authentication model using hand gesture images.

An object of the present invention, but relates to personal identification in environments where non-contact is required, use of which is not clear position shift finger vein pattern image, to realize personal identification with high accuracy. In order to achieve the above object, and as a method of extracting a vein pattern contained in the means for acquiring a finger vein pattern image by a non-contact, and the obtained image using the outline of the finger based on the hand and means for performing a rotation correction, and statistically this means for obtaining the whole vein pattern with the strong characteristic in the blood vessel pattern shown by repeating the means for normalizing the location of the finger image, a visit to any position arbitrary dark by the luminance point length portion in the image and matching means for comparing only the parts, dividing the image into small regions and in each small region by performing the matched independently, and a means for evaluating positional deviations where matching is recognized. Finger vein pattern input interface, a light source, an optical filter, CCD camera, image capture board, the finger insertion opening, identification claimant, automatic doors, the password input keys, a fingerprint input interface, and an iris image pick-

Personal identification apparatus

Handwritten signature is being used in various applications on daily basis. The problem arises when someone decides to imitate our signature and steal our identity. Therefore, there is a need for adequate protection of signatures and a need for systems that can, with a great degree of certainty, identify who is the signatory. This paper presents previous work in the field of signature and writer identification to show the historical development of the idea and defines a new promising approach in handwritten signature identification based on some basic concepts of graph theory. This principle can be implemented on both on-line handwritten signature recognition systems and off-line handwritten signature recognition systems. Using graph norm for fast classification (filtration of potential users), followed by comparison of each signature graph concepts value against values stored in database, the system reports 94.25% identification accuracy.

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Handwritten signature identification using basic concepts of graph theory

The idea behind this article is to give an overview of B-tree data structure and show the connection between B-tree indexing technique and computer forensics. B-tree is a fast data indexing method that organizes indexes into a multi-level set of nodes, where each node contains indexed data. This technique is most commonly used in databases and file systems where it is important to retrieve records stored in a file when data is to large to fit in main memory. In that case, Btrees are used to reduce the number of disk accesses.

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Analysis of B-tree data structure and its usage in computer forensics

Aging process affects the appearance of people in different ways One of the changes caused by aging are changes in craniofacial morphology of individuals Different models of facial aging exist and this paper will apply anthropometric model of facial aging on pictures of 20 people at different ages of their lives to show the similarities between craniofacial morphology of people at the same age FGNet database that contains two-dimensional faces of people from childhood to adulthood will be used for analysis

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Application of modified anthropometric model in facial age estimation

Researchers in the field of biometrics found that human hand contains some characteristics that can be used for personal identification. Some of these are hand geometry and hand shape. These biometric characteristics are more than 30 years old and are very popular and widely accepted among people. That makes them suitable for development of various acquisition and authentication methods. So far, many contact-based and contact-less systems have been developed in the academy and commercial sector. Contact-less biometrics is becoming more important in this field, thus making the researchers to concentrate their efforts in this direction.

Trends in Hand Geometry Biometrics

Age estimation of humans is one of the problems in the field of computer vision which is insufficiently researched. To efficiently estimate the age of an individual based on his/her face image, characteristic points of the face have to be determined. In this paper, the most common face recognition algorithms, like Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and Elastic Bunch Graph Matching (EBGM) will be described. These algoritms will then be compared, and the application in age estimation will be given. At the end of the paper, ideas for future research will be stated.

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Petra Koruga

Papers

Handwritten signature identification using basic concepts of graph theory

Analysis of B-tree data structure and its usage in computer forensics

Application of modified anthropometric model in facial age estimation

Trends in Hand Geometry Biometrics

A review of face recognition algorithms and their application in age estimation