Hand recognition is one of the popular biometry methods for access control systems. In this paper, a new scheme for personal recognition using thermal images of the hand and an extension neural network (ENN) is presented. The features of the recognition system are extracted from gray level hand images, which are taken by an infrared camera. The main advantage of the thermal image is that it can reduce errors and noise in the features extracted stage, which is most important to increase the accuracy of recognition systems. Moreover, a new recognition method based on the ENN is proposed to perform the core functions of the hand recognition system. The proposed ENN-based recognition method also permits rapid adaptive processing for a new pattern, as it only tunes the boundaries of classified features or adds a new neural node. It is feasible to implement the proposed method on a Microcomputer for a portable personal recognition device. From the tested examples, the proposed method has a significantly high degree of recognition accuracy and shows good tolerance to errors added.
Person recognition and verification is a very important function in many access control systems. Biometric technology is a new method for recognizing the identity of a person based on an already established database of physiological or behavioral characteristics [
To improve the problems of the traditional technologies, this paper proposes a new hand-geometry-based recognition system, which uses an infrared camera to acquire the thermal image of user’s hands. There are some advantage of the proposed scheme as follows: (1) without the problem of light interference, photographs can be taken under areas of inadequate lighting; (2) infrared cameras can detect radiation heat emanated from human hands; hence, it is not affected by different lengths of palms, dirt, or wounds; thus, it does not lead to errors and lower discriminability; (3) thermal imaging can be taken by a noncontact and noninvasive image capture devices that can avoid causing any uncomfortable feelings or hygienic concerns of users. Therefore, this paper presents a thermal imaging method to capture hand images, then using the Otsu method extracts the hand features from the gray level images. Moreover, this paper proposes a new recognition method based on ENN to perform the core functions of the hand recognition system. The proposed ENN-based recognition method [
The operation of the hand-based recognition system is similar to other biometric authentication devices: sample acquisition, feature extraction, data storage, comparison, and verification. The structure of the proposed hand recognition system is shown in Figure
The structure of the proposed hand-based recognition system. acquiring device was designed, as shown in Figure
The acquired device of the thermal image of palmar surface.
The palmar boundary must be detected for capturing the characteristics of the palmar shape. The boundary can be detected by scanning the pixel points, where the scanning is divided into two stages. At stage 1, the images are scanned vertically from top to bottom, and the pixel points move from the left to the right. If the left and right adjacent gray values of the pixel points are all greater than 0, the locations of the pixel coordinates are memorized and expressed by white points. However, some places are missed in detection. Hence, at stage 2, the images are scanned from the left to the right in the horizontal direction, and the pixel points move from top to bottom. If the upper and adjacent gray values of the pixel points are all greater than 0, the locations of the pixel coordinates are also memorized. After the scanning action of the two stages, the palm edge coordinates can be thoroughly detected.
The palm edge coordinates contain the valley points and peak points of fingers, as shown in Figure
Coordinates of valley points and peak points.
Distance between contour and point
In the clockwise direction, the palm contour coordinates
Distribution condition of the distance between palmar contour coordinates and point
The distance distribution in Figure
Additional valley points.
Feature of palm distance distribution.
For the feature extraction of palmar shapes, there are a total of 34 features, which become fixed features after reaching a certain age; hence, they can be used as recognition features. The proposed features in this paper are summarized in Table
Palm shape features.
Item | Feature |
---|---|
1 | Hand contour |
2 | Palm contour |
3 | Horizontal distance of gravity |
4 | Vertical distance of gravity |
5 | Distance between gravity and thumb |
6 | Distance between gravity and index finger |
7 | Distance between gravity and middle finger |
8 | Distance between gravity and third finger |
9 | Distance between gravity and little finger |
10 | Contour of thumb |
11 | Thumb length |
12 | Thumb width |
13 | |
14 | |
15 | Contour of index finger |
16 | Length of index finger |
17 | Width of index finger |
18 | |
19 | |
20 | Contour of middle finger |
21 | Length of middle finger |
22 | Width of middle finger |
23 | |
24 | |
25 | Contour of third finger |
26 | Length of third finger |
27 | Width of third finger |
28 | |
29 | |
30 | Contour of little finger |
31 | Length of little finger |
32 | Width of little finger |
33 | |
34 |
After the gravity coordinate is determined, the distribution features of palm gravity distances are extracted, including the distance between the gravity coordinate and valley points of each finger, the distance between the gravity coordinate and the midpoints of valley points, and the horizontal and vertical distances between the gravity coordinate and palm contour, as shown in Figure
Finger length features.
In addition, three widths of each finger are extracted and regarded as features. As shown in Figure
Finger width features.
Finger size features.
At the recognition stage the inputting patterns are compared with the patterns stored in the system database. Learning from a set of training patterns is an important feature of most pattern recognition systems. The neural networks are usually used for pattern recognition; the advantage of a neural network over other classifiers is that it can acquire experience from the training data, but the training data must be sufficient and compatible to ensure proper training, its convergence of learning is influenced by the network topology and values of learning parameters. To overcome the limitations of the multilayer neural network (MNN) mentioned [
In this clustering problem of hand recognition, hand’s features and associated person types cover a range of values. Therefore, using the ENN is most appropriate for hand recognition. The schematic structure of the ENN is depicted in Figure
The structure of extension neural network (ENN).
The learning of the ENN is to tune the weights of the ENN to achieve good clustering performance or to minimize the clustering error. Before the learning, several variables have to be defined. Let training set
Set the connection weights between input nodes and output nodes according to the range of classical domains. The range of classical domains can be directly obtained from previous experience, or determined from training data as follows:
Read the
Use the extension distance (ED) to calculate the distance between the input pattern
The proposed extension distance is a new distance measurement; it can be graphically presented as in Figure
The proposed extension distance (ED).
Find the
Update the weights of the
Repeat Step
Stop, if the clustering process has converged, or the total error has arrived at a preset value, otherwise, return to Step
It should be noted that the proposed ENN can take human expertise before the learning, and it can also produce meaningful output after the learning, because the classified boundaries of the features are clearly determined.
Read the weight matrix of the ENN.
Read a testing pattern
Use the proposed extension distance (ED) to calculate the distance between the tested pattern and every existing cluster by (
Find the
Stop, if all the tested patterns have been classified; otherwise go to Step
To demonstrate the proposed method, 600 sets of palmar images with 30 persons were used to test the proposed method. In this case, the structures of the proposed ENN are 30 output nodes and 34 input nodes. If the system randomly chooses 300 instances from the hand image as the training data set, the rest of the instances of the hand image are the testing data sets. The proposed hand recognition system is implemented in a PC with the Visual Basic; the recognition window of the proposed systems is shown in Figure
The proposed hand recognition system.
When capturing hand images, the surrounding environment and lighting can affect the quality of the hand images, causing wrong identifications when poor images have been acquired. Earlier hand identification systems employed CCD cameras and have the following disadvantages: (1) CCD can only produce images in daylight and cannot be used in the dark. A lighting device must be installed to solve this problem; (2) when the traditional CCD identifies hand-shaped characteristics, errors may occur and the identification rate may be lowered due to different lengths of fingernails, dirt, or wounds present during feature extraction. Figure
The errors of the input features with different cameras.
The errors of the infrared camera
The errors of the traditional CCD camera
When traditional biometric systems capture image features, there may be offsets in angles or positions of palm as the user is unfamiliar with the method of application or nervous, causing errors when capturing features; thus, the status identified may be incorrect. Figure
Thermal images of hand of the same person from different finger’s angles.
The errors of the input feature with different finger’s angles.
In this paper, the total training samples are 300 sets, and the total testing samples are 300 sets with 30 persons. Table
Recognition performances of different methods.
Compare items | Recognition methods | ||||
ENN | MNN-1 | MNN-2 | MNN-3 | K-means | |
Structure | 34-30 | 34-33-30 | 34-34-30 | 34-35-30 | N/A |
No. of connections | 2040 | 2112 | 2176 | 2240 | N/A |
Learning times (epochs) | 86 | 1000 | 1000 | 1000 | N/A |
Learning accuracy | 100% | 91% | 93% | 95% | 70% |
Testing accuracy | 99% | 88% | 90% | 86% | 68% |
This paper presents a novel hand recognized method based on the ENN for biometric authentication. This study applied a thermal imaging camera to capture the palmar images to develop the person’s recognition system; the average errors of the input features with thermal camera are smaller than average errors of the using traditional CCD camera. According to the experimental results, the proposed recognized features show that the errors caused by the open angle of fingers can be reduced. Compared with other existing methods, the structure of the proposed ENN-based method is simpler and the learning time is faster than other methods. Moreover, the proposed ENN-based hand recognized method also permits fast adaptive processes for the new data, which only tune the boundaries of classified features or add a new neural node. It is feasible to implement the proposed method in a Microcomputer for portable fault detecting devices. We hope this paper will lead to further investigation for industrial applications.