• 한국멀티미디어학회논문지
• /
• 제17권8호
• /
• pp.937-945
• /
• 2014

Multi-modal biometric system can use another biometric trait in the case of having deficiency at a biometric trait. It also has an advantage of improving the performance of personal identification by using multiple biometric traits, so studies on new biometric traits have continuously been performed. The inner face of finger is a relatively new biometric trait. It has two major features of knuckle lines and wrinkles, which can be used as discriminative features. This paper proposes a finger identification method based on displacement vector to effectively process some variation appeared in contactless hand image. At first, the proposed method produces displacement vectors, which are made by connecting corresponding points acquired by matching each pair of local block. It then recognize finger by measuring the similarity among all the detected displacement vectors. The experimental results using pubic CASIA hand image database show that the proposed method may be effectively applied to personal identification.

• Asian-Australasian Journal of Animal Sciences
• /
• 제28권3호
• /
• pp.311-317
• /
• 2015

Phenotypic characterization and body biometric in 13 traits (height at withers, body length, chest girth, paunch girth, ear length, tail length, length of tail up to switch, face length, face width, horn length, circumference of horn at base, distances between pin bone and hip bone) were recorded in 233 adult Gojri buffaloes from Punjab and Himachal Pradesh states of India. Traits were analysed by using varimax rotated principal component analysis (PCA) with Kaiser Normalization to explain body conformation. PCA revealed four components which explained about 70.9% of the total variation. First component described the general body conformation and explained 31.5% of total variation. It was represented by significant positive high loading of height at wither, body length, heart girth, face length and face width. The communality ranged from 0.83 (hip bone distance) to 0.45 (horn length) and unique factors ranged from 0.16 to 0.55 for all these 13 different biometric traits. Present study suggests that first principal component can be used in the evaluation and comparison of body conformation in buffaloes and thus provides an opportunity to distinguish between early and late maturing to adult, based on a small group of biometric traits to explain body conformation in adult buffaloes.

• Journal of Information Processing Systems
• /
• 제10권4호
• /
• pp.555-567
• /
• 2014

Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.

• Asian-Australasian Journal of Animal Sciences
• /
• 제24권4호
• /
• pp.449-456
• /
• 2011

Eighteen different biometric traits in 407 Kankrej cows from their breeding zone, i.e. Palanpur district of Gujarat, India, were recorded and analyzed by factor analysis to explain body conformation. The averages of body length, height at withers, height at shoulder, height at knee, heart girth, paunch girth, face length, face width, horn length, horn diameter, distance between horns, ear length, ear width, neck length, neck diameter, tail length with switch, tail length without switch and distance between hip bones were $123.44{\pm}0.37$, $124.49{\pm}0.28$, $94.68{\pm}0.30$, $38.2{\pm}0.14$, $162.56{\pm}0.56$, $178.95{\pm}0.70$, $44.09{\pm}0.10$, $15.91{\pm}0.05$, $42.47{\pm}0.53$, $26.07{\pm}0.19$, $13.34{\pm}0.08$, $31.24{\pm}0.12$, $16.10{\pm}0.05$, $50.63{\pm}0.18$, $73.21{\pm}0.32$, $111.62{\pm}0.53$, $89.34{\pm}0.34$ and $17.28{\pm}0.10\;cm$, respectively. The correlation coefficients between different traits ranged from -0.806 (horn diameter and distance between horns) to 0.815 (heart girth and paunch girth). Most of the correlations were positive and significant. Factor analysis with promax rotation with power 3 revealed three factors which explained about 66.02% of the total variation. Factor 1 described the cow body and explained 38.89% of total variation. The second factor described the front view/face of the cow and explained 19.68% of total variation. The third factor described the back of the cow and explained 7.44% of total variation. It was necessary to include some more variables for factor 3 to obtain a reliable estimate of the back view of the cow. The lower communities shown for distance between horns, horn diameter, ear width and neck diameter indicated that these traits did not contribute effectively to explaining body conformation and can be dropped from recording, whereas all other traits are important and needed to explain body conformation in Kankrej cows. The result suggests that principal component analysis (PCA) could be used in breeding programs with a drastic reduction in the number of biometric traits to be recorded to explain body conformation.

• 디지털융복합연구
• /
• 제11권11호
• /
• pp.795-801
• /
• 2013

과거의 오프라인에서만 이루어졌던 생활이 온라인에서의 활동으로 발전함으로 인해 온라인상에서 사용자가 올바른 사용자인지의 여부는 중요한 문제이다. 온라인상이나 나아가 일반 생활에서도 사용자 인증을 보다 정확하게 하기 위하여 생체인식 기술을 도입하고 있다. 생체인식 기술은 개인의 고유한 특징을 이용하여 인증을 수행하는 방법으로 비밀번호를 대체하는 차세대 인증 기술로 각광받고 있다. 인간의 고유한 특징의 종류는 매우 다양하며 이러한 특징을 추출하는 생체인식 기술도 다양한 장치와 알고리즘을 이용하여 이루어진다. 본 논문에서는 첫째로 이러한 다양한 장치인 스마트폰, 스마트와치, M2M 플랫폼을 분석하고 적용하였을 경우 어떤 효용성이 있는지 분석한다. 둘째로, 다른 장치 플랫폼에서 포괄적인 인증인 효용성기반의 AIB를 제안한다. 제안 인증기법은 신체정보를 이용한 효율적인 인증을 포함한다.

• International Journal of Computer Science & Network Security
• /
• 제21권11호
• /
• pp.321-327
• /
• 2021

Mobile devices have recently developed to be an integral part of humans' daily lives because they meet business and personal needs. It is challenging to design a feasible and effective user authentication method for mobile devices because security issues and data privacy threats have significantly increased. Biometric approaches are more effective than traditional authentication methods. Therefore, this paper aims to analyze the existing biometric user authentication methods on mobile platforms, particularly those that use face recognition, to demonstrate the methods' feasibility and challenges. Next, this paper evaluates the methods according to seven characteristics: universality, uniqueness, permanence, collectability, performance, acceptability, and circumvention. Last, this paper suggests that solely using the method of biometric authentication is not enough to identify whether users are authentic based on biometric traits.

• Journal of Information Processing Systems
• /
• 제14권5호
• /
• pp.1114-1135
• /
• 2018

A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual super-classifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.

• 전자공학회논문지CI
• /
• 제45권5호
• /
• pp.162-172
• /
• 2008

본 논문에서는 이동환경에서 개인의 신원을 인증하는 수단으로 치열영상과 음성을 생체정보로 이용한 멀티모달 화자인증 방법에 대하여 제안한다. 제안한 방법은 이동환경의 단말장치중의 하나인 스마트폰의 영상 및 음성 입력장치를 이용하여 생체 정보를 획득하고, 이를 이용하여 사용자 인증을 수행한다. 더불어, 제안한 방법은 전체적인 사용자 인증 성능의 향상을 위하여 두 개의 단일 생체인식 결과를 결합하는 멀티모달 방식으로 구성하였고, 결합 방법으로는 시스템의 제한된 리소스를 고려하여 비교적 간단하면서도 우수한 성능을 보이는 가중치 합의 방법을 사용하였다. 제안한 멀티모달 화자인증 시스템의 성능평가는 스마트폰에서 획득한 40명의 사용자에 대한 데이터베이스를 이용하였고, 실험 결과, 치열영상과 음성을 이용한 단일 생체인증 결과는 각각 8.59%와 11.73%의 EER를 보였으며, 멀티모달 화자인증 결과는 4.05%의 EER를 나타냈다. 이로부터 본 논문에서는 인증 성능을 향상하기 위하여 두 개의 단일 생체인증 결과를 간단한 가중치 합으로 결합한 결과, 높은 인증 성능의 향상을 도모할 수 있었다.

• International Journal of Computer Science & Network Security
• /
• 제24권9호
• /
• pp.202-206
• /
• 2024

Robust system may not guaranty its applicability and adaptability. That is why research and development go together in the modern research concept. In this paper we are going to examine the applicability and adaptability of gait-based biometric identity verification system especially in the GCC (Gulf Cooperation Council). The system itself closely involved with human interaction where privacy and personality are in concern. As of 1^st phase of our research we will establish gait-based identity verification system and then we will explain them in and out of human interaction with the system. With involved interaction we will conduct an extensive survey to find out both applicability and adoptability of the system. To conduct our experiment, we will use UCMG databased [1] which is readily available for the research community with more than three thousand video sequences in different viewpoint collected in various walking pattern and clothing. For the survey we will prepare questioners which will cover approach of data collection, potential traits to collect and possible consequences. For analyzing gait biometric trait, we will apply multivariate statistical classifier through well-known machine learning algorithms in a ready platform. Similarly, for the survey data analysis we will use similar approach to co-relate the user view point for such system. It will also help us to find the perception of the user for the system.

• 한국멀티미디어학회논문지
• /
• 제22권12호
• /
• pp.1347-1355
• /
• 2019

Deep learning approach based on convolution neural network (CNN) has extensively studied in the field of computer vision. However, periocular feature extraction using CNN was not well studied because it is practically impossible to collect large volume of biometric data. This study uses the ResNet model which was trained with the ImageNet dataset. To overcome the problem of insufficient training data, we focused on the training of multi-layer perception (MLP) having simple structure rather than training the CNN having complex structure. It first extracts features using the pretrained ResNet model and reduces the feature dimension by principle component analysis (PCA), then trains a MLP classifier. Experimental results with the public periocular dataset UBIPr show that the proposed method is effective in person authentication using periocular region. Especially it has the advantage which can be directly applied for other biometric traits.