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This Paper describes a hand geometry biometric identification system. We have acquired a database of 22 people using a conventional
document scanner. The experimental section consists of a study about the discrimination capability of different extracted
features, and the identification rate using different classifiers based on neural networks.
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... have trained a Multi-Layer Perceptron (MLP) [9] as discriminative classifier in the following fashion: when the input data belongs to a genuine person, the output (target of the NNET) is fixed to 1. When the input is an impostor person, the output is fixed to -1. Figure 8 shows the neural network architecture. We have used a MLP with 30 neurons in the hidden layer, trained with the Levenberg-Marquardt algorithm, which computes the approximate Hessian matrix, because it is faster and achieves bet- ter results than the classical back-propagation algorithm. ...
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Citations
... o identity is claimed from the person. The automatic system must determine who is trying to access. b) Verification: In this approach the goal of the system is to determine whether the person is who he/she claims to be. This implies that the user must provide an identity and the system just accepts or rejects the users according to a successful or Marcos Faundez-Zanuy. 2005. Study of a committee of neural networks for biometric hand-geometry recognition. In Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems (IWANN'05). Springer-Verlag, Berlin, Heidelberg, 1180-1187. DOI: https://doi.org/10.1007/11494669_145 _____________________ ...
... Without loss of generality we will use a set of features and a database extracted from hand-geometry images obtained from a 22 people and 10 different realizations per person set (5 for training and 5 for testing). The feature extraction section and the digital signal input (blocks 1 and 2 of figure 1) can be found in [6]. Our matching algorithm will be a neural network or a committee of neural networks. ...
... In pattern recognition applications it is well known that a number of differently trained neural networks (that can be considered as "experts"), which share a common input, can produce a better result if their outputs are combined to produce an overall Marcos Faundez-Zanuy. 2005. Study of a committee of neural networks for biometric hand-geometry recognition. In Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems (IWANN'05). Springer-Verlag, Berlin, Heidelberg, 1180-1187. DOI: https://doi.org/10.1007/11494669_145 _____________________ ...
This Paper studies different committees of neural networks for biometric pattern recognition. We use the neural nets as classifiers for identification and verification purposes. We show that a committee of nets can improve the recognition rates when compared with a multi-start initialization algo-rithm that just picks up the neural net which offers the best performance. On the other hand, we found that there is no strong correlation between identifi-cation and verification applications using the same classifier.
... Feature extraction is relatively simply, so low-cost processors can be used. It can be as simple as contour extraction with the scheme [18] shown in figure 7, and measurements such as lengths, perimeters and areas. Figure 8 shows some measured features. ...
This paper presents an overview of the main topics related to biometric security technology, with the main purpose to provide a primer on this subject. Biometrics can offer greater security and convenience than traditional methods for people recognition. Even if we do not want to replace a classic method (password or handheld token) by a biometric one, for sure, we are potential users of these systems, which will even be mandatory for new passport models. For this reason, to be familiarized with the possibilities of biometric security technology is useful.
... Hand geometry recognition systems comprise several steps, such as: Images acquisition; Preprocessing the images; Detection and measurement of the feature points; Features extraction, including the construction of the data base with the signatures of persons, and lastly the recognition. Different techniques, apply different commitments in relation to each step above as the works, [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34]. Furthermore there are others approaches in the literature which leads the investigations considering different extracted features of the hand, by biometrics, for instance, the palm print [32], [35], [38], the hand gesture [39] and the hand shape [23], [37], [40]. ...
In data analysis, the hierarchical clustering algorithms are powerful tools allowing to identify natural clusters, often without any priori information of the data structure, and are quite often used because provide a graphical representation of the resulting partitions, a hierarchy or dendrogram, revealing more information than non-hierarchical algorithms that returns a unique partition. Moreover, it is not necessary specify the number of clusters à priori. Cutting the dendrogram in different levels on the hierarchy produces different partitions and also, the use of different clusters aggregation methods for the same data set can produces different hierarchies and hence different partitions. So, several studies have been concerned with validate the resulting partitions comparing them, for instance, by the analysis of cohesion and separation of their clusters. The work presented here focuses on the problem of choosing the best partition in hierarchical clusterings. The procedure to search for the best partition is made in the nested set of partitions, defined by the hierarchy. In traditional approaches each partition is defined by horizontal lines cutting the dendrogram at a determined level. Was proposed an improved method, SEP/COP, to obtain the best partition, based on a wide set of partitions. In this paper we discuss these two types of approaches and we do a comparative study using a set of experiments using two-dimensional synthetics and real-world data sets, based on the biometrics of the hands. This database is provided from Bosphorus Hand Database, in the context of recognition of the identity of a person by using the features of her hand/biometrics. In the results of the experiments, the SEP/COP showed to be a better partition algorithm in some situations namely regarding to real data, leading to a contribution to identification systems based on the biometrics of the hands shape.
... More and more crimes related to password and card thefts are being reported every day. These problems are not trivial and new means of authentications are not trivial and new means of authentications which are more user friendly and less prone to be duplicated is required [2]. ...
In this method, the hand geometry measurement be used and a new features has been extracted
from two landmarks determined at the first step of preprocessing . The images taken by scanner
covered by a black box without peg. The data collected and then the pre-processing applied on
it, after that passed to feature extraction stage. Each person has its own features like finger
length, finger width, palm width, angle value and many other measurements. Features extraction
contain 20 distances for fingers and palm (D1-D20) and 10 angels values measured and
determine the center point on it . The angels calculated for a ten triangles determined, then
calculate the Area and the Perimeter on it. The second part of the proposed method begin with
training a neural network. The BPNN used here with trainlm algorithm after that testing the
NN. The population size was 100 samples, for 10 person, each person have 10 samples. For
Training set 70 samples taken and 30 for testing the NN.
The Recognition Rate (RR) was 91%.
... Hand geometry has a set of points that are crucial for the extraction of some of the main features used for biometric recognition. These characteristic points correspond to the five fingertips and the four gaps (valleys) between them [10,12]. Their detection corresponds to one of the most difficult phases in a recognition system. ...
Hand-geometry biometric recognition is normally based on the detection of five points that correspond to the fingertips and four points between them (valley points). Specific methods often have to be implemented during the acquisition stage to make the detection of those points easier. This study presents techniques that have been developed to overcome the difficulties and limitations of the current systems. Moreover, a hand-geometry based recognition system that has no constraints during image acquisition is presented.
A methodology was developed based on the hand skeleton for the points on the fingertips and for the valley points it was based on the curvature of the hand contour. The principal difficulties were found during the segmentation step, which often fails if the fingers are not spread out. Once the points have been located, the necessary features for authentication were extracted. Classification algorithms were implemented at this stage. Those showing the best results presented a Genuine Acceptance Rate (GAR) of 76% and 8% for the False Acceptance Rate (FAR).
... At present, many hand geometry authentication systems are implemented both in identification and verification tasks due to the ease of sample collection, relatively low hardware costs, and availability of various feature extraction algorithms . Specifically, two system environments are designed, shown in Figure 1, i.e., a peg-fixed system [24] [15] [26] [29] and a peg-free system [5] [9] [6] [8] [20] [16] [1] [27] [28] [19] [3]. The peg-fixed system uses pins attached on a plate to align hand's position properly. ...
... At present, many hand geometry authentication systems are implemented both in identification and verification tasks due to the ease of sample collection, relatively low hardware costs, and availability of various feature extraction algorithms. Specifically, two system environments are designed, shown in Figure 1, i.e., a peg-fixed system [24,15,26,29] and a peg-free system [5,9,6,8,20,16,1,27,28,19,3]. The peg-fixed system uses pins attached on a plate to align hand's position properly. ...
At present, applications of biometrics are rapidly increasing due to inconveniences in using traditional passwords and physical keys. Hand geometry, one of the most well-known biometrics, is implemented in many verification systems with various feature extraction methods. In recent work, a hand geometry verification system using time series conversion techniques and Dynamic Time Warping (DTW) distance measure with Sakoe-Chiba band has been proposed. This system demonstrates many advantages, especially ease of implementation and small storage space requirement using time series representation. In this paper, we propose a novel hand geometry verification system that exploits DTW distance measure and R-K band learning to further improve the system performance. Finally, our evaluation reveals that our proposed system outperforms the current system by a wide margin, in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR), and Total Success Rate (TSR) at Equal Error Rate (EER). Comment: In Proceedings of 11th National Computer Science and Engineering Conference (NCSEC 2007), Bangkok, Thailand, 19/11/2007. 8 pages, 10 figures
... Feature extraction is relatively simply, so low-cost processors can be used. It can be as simple as contour extraction with the scheme [18] shown in figure 7, and measurements such as lengths, perimeters and areas. Figure 8 shows some measured features. ...
The word biometrics comes from the Greek words “bios” (life) and “metrikos” (measure). Strictly speaking, it refers to a science involving the statistical analysis of biological characteristics. Thus, we should refer to biometric recognition of people, as those security applications that analyze human characteristics for identity verification or identification. However, we will use the short term “biometrics” to refer to “biometric recognition of people”. Biometric recognition offers a promising approach for security applications, with some advantages over the classical methods, which depend on something you have (key, card, etc.), or something you know (password, PIN, etc.). A nice property of biometric traits is that they are based on something you are or something you do, so you do not need to remember anything neither to hold any token.
... Feature extraction is relatively simply, so low-cost processors can be used. It can be as simple as contour extraction with the scheme [18] shown in figure 7, and measurements such as lengths, perimeters and areas. Figure 8 shows some measured features. ...
This paper presents an overview of the main topics related to biometric security technology, with the central purpose to provide a primer on this subject. Biometrics can offer greater security and convenience than traditional methods for people recognition. Even if we do not want to replace a classic method (password or handheld token) by a biometric one, for sure, we are potential users of these systems, which will even be mandatory for new passport models. For this reason, it is useful to be familiarized with the possibilities of biometric security technology
... We can observe that just the output number k is activated, and the number of outputs is equal to the number of users. Table 4 shows the output codes (targets) where each user has his own code, and these codes are selected from the first 22 BCH (15,7) codes. In fact, BCH (15,7) yields up to 7 2 128 = output codes. ...
... Table 4 shows the output codes (targets) where each user has his own code, and these codes are selected from the first 22 BCH (15,7) codes. In fact, BCH (15,7) yields up to 7 2 128 = output codes. However, we just need 22, because this is the number of users. ...
... k BCH (15,7) code 1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 2 0,0,0,0,0,0,1,1,1,0,1,0,0,0,1 3 0,0,0,0,0,1,0,0,1,1,1,0,0,1,1 4 0,0,0,0,0,1,1,1,0,1,0,0,0,1,0 5 0,0,0,0,1,0,0,1,1,1,0,0,1,1,0 6 0,0,0,0,1,0,1,0,0,1,1,0,1,1,1 7 0,0,0, 0,1,1,0,1,0,0,1,0,1,0,1 8 0,0,0,0,1,1,1,0,1,0,0,0,1,0,0 9 0,0,0,1,0,0,0,0,0,0,1,1,1,0,1 10 0,0,0,1,0,0,1,1,1,0,0,1,1,0,0 11 0,0,0,1,0,1,0,0,1,1,0,1,1,1,0 12 0,0,0,1,0,1,1,1,0,1,1,1,1,1,1 13 0,0,0,1,1,0,0,1,1,1,1,1,0,1,1 14 0,0,0,1,1,0,1,0,0,1,0,1,0,1,0 15 0,0,0,1,1,1,0,1,0,0,0,1,0,0,0 16 0,0,0,1,1,1,0,1,0,0,0,1,0,0,0 17 0,0,0,1,1,1,1,0,1,0,1,1,0,0,1 18 0,0,1,0,0,0,0,0,0,1,1,1,0,1,0 19 0,0,1,0,0,0,1,1,1,1,0,1,0,1,1 20 0,0,1,0,0,1,0,0,1,0,0,1,0,0,1 21 0,0,1,0,0,1,1,1,0,0,1,1,0,0,0 22 0,0,1,0,1,0,0,1,1,0,1,1,1,0,0 Table 4. 22 Output codes for BCH (15,7) approach. ...
This paper describes a hand geometry biometric identification system. We have acquired a database of 22 people, 10 acquisitions per person, using a conventional document scanner. We propose a feature extraction and classifier. The experimental results reveal a maximum identification rate equal to 93.64%, and a minimum value of the detection cost function equal to 2.92% using a multi layer perceptron classifier.
... Without loss of generality we will use a set of features and a database extracted from hand-geometry images obtained from a 22 people and 10 different realizations per person set (5 for training and 5 for testing). The feature extraction section and the digital signal input (blocks 1 and 2 offigure 1) can be found in [6]. Our matching algorithm will be a neural network or a committee of neural networks. ...
This Paper studies different committees of neural networks for biometric pattern recognition. We use the neural nets as classifiers
for identification and verification purposes. We show that a committee of nets can improve the recognition rates when compared
with a multi-start initialization algorithm that just picks up the neural net which offers the best performance. On the other
hand, we found that there is no strong correlation between identification and verification applications using the same classifier.
