With the advancement in information technology, personal recognition systems have attracted wide attention. With more options of the recognition systems, the recognition rate and price become very important. This paper used palmprint with the extension method to design a low-cost personal recognition system. First, this paper uses a low-cost webcam to capture the image of palmprints, here the length, slop, and distance of principal line of palmprints can be captured by the image process method. Generally, the devices for capturing hand images should have higher-resolution, so their prices are higher. This paper used a low-pixel and low-cost webcam as the capturer, and it had also a high recognition rate that is equivalent in high resolution devices. The recognition algorithm of this study used extension algorithm for hand recognition and was compared with other traditional algorithms and recognition systems. Finally, the experimental results showed that the method proposed in this study has higher recognition rate than traditional algorithms and proved that low-resolution and low-cost capture tools have a high recognition rate as well.
The biological recognition technology using human body as features gradually replaced traditional personal recognition technology. The traditional personal recognition systems may easily be lost, stolen, or forgotten; the “biological recognition technology” using human body as features has become a new trend [
Hand features can be divided into three major parts—fingerprint, palmprint, and hand geometry. This study captured the major hand feature, palmprint, for identification. The slopes, lengths, and position distances of three principal lines of palmprints were the main features. Fingerprint and fine palmprint are most likely to become vague because of external factors and aging. The uniqueness of hand geometry is lower than the former two features, and it is an unstable factor. The palmprint is unlikely to be indistinct because of age and external factors; thus, it has high stability and uniqueness. This study used a low-resolution webcam to capture and transfer images to the computer for research. Different capture instruments and different recognition methods were compared and discussed.
Figure
The structure of the proposed palmar recognition system.
For capturing the hand image, the subject’s fingers should be unfolded naturally, and five fingers cling to the bottom. The fingers should not contact each other or be very close to each other. If the fingers are put together or very close to each other, there will be errors or faults as the captured length, width, and contour features of fingers are influenced, as shown in Figure
Images of hand with contacting or too close fingers.
The tool for capturing hand images used in this study is a charge-coupled device. It is very sensitive to light; as long as there is slight change in the light and shadow, there will be large differences in the captured feature values, as shown in Figure
Hand image with different lights.
Light is too dark
Light moderate
Light is too bright
Since the background noise of hand image is likely to cause misrecognition, the original hand image is separated from the background. The image is grayed, and then the background is expressed as gray-scale value 0 (black). The Otsu’s method is used in this paper [
If the gray-scale value range of image is
The threshold
If there is a
Using Otsu’s method to separate palm from background.
Original image
Image separated from background
When the palm is separated from the background by using the abovementioned method, the palmar edge is detected. The pixels are scanned from top to bottom and from left to right to ensure that the full image is scanned. During scanning, if the left and right gray-scale values are greater than 0, the pixel is colored red. When the scan in two directions is finished, the complete palmar edge coordinates can be detected, as shown in Figure
Scanning direction of hand image.
When the palmar contour coordinates are determined, in order to capture geometric features and palmprint range from hand, the tip points and valley points of fingers should be defined in the palmar edge coordinate set
Coordinates of valley points and tip points.
Distance between contour and point
According to the distance distribution in Figure
Distribution condition of the distance between palmar contour coordinates and point
The characteristic of tip points of palmar contour in distance
According to the valley point coordinates
The new coordinates (
The abovementioned four points are connected to form a square, this square is located as the palmprint region [
Image of the palmprint range.
The region image processing is also known as image filter or mask image processing. The image range is mostly
Vertical detect realized by
Marginalized palmprint range.
Each pixel of gray-scale value processed image has its own gray-scale value. The image binarization means to set a threshold
Marginalization of the palmprint range image.
The low pass filter
The
Mask for Sobel operation.
Palmprint Sobel operation processed.
The line method aims to change three principal palmprint lines to three straight lines to reserve, and the noise is eliminated, so as to capture principal palmprint line features directly.
The block is sought for at the beginning: first, the pixel (0) is detected in the Sobel operated palmprint range, and then the pixels adjacent to the whole palmprint range are sought for, it is a block; and then each block is changed into the longest line. At this point, the palmprint range forms multiple straight lines, it is the line map, as shown in Figure
Line map.
Principal palmprint lines.
The features can be captured directly after image preprocessing, including slope, length, and distance. The captured palmprint features are shown in Table
Palmprints features.
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Circumference of the palmprint range |
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The slope of the heart line |
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The slope of the head line |
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The slope of the life line |
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The line between valley and heart line |
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The line between valley and head line |
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The line between valley and life line |
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Length of heart line 1 |
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Length of heart line 2 |
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Length of head line 1 |
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Length of head line 2 |
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Length of life line 1 |
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Length of life line 2 |
Features of the palmprint lines.
Palmprint range perimeter: the valley points of fingers form a square enclosing the range of palmprint features to be captured. The perimeter of this range is classified as a feature as shown in Figure
Principal line slope of palmprint: the image preprocessing contains line segmentation, as long as any two pixels are determined in each reserved principal line. The needed principal line slope can be obtained, as shown in Figure
Distance to principal line of palmprint: in the image of principal lines of palmprint in the palmprint range, the vertex of palmprint range used in the paper. As mentioned above, the valley point between ring finger and little finger is one of vertices in palmprint range, connected to the vertex on the cross. This straight line then passes through three principal lines. The distances between the starting point of this straight line—the valley point between ring finger and little finger and three principal lines are three palmprint features.
Two lengths of principal line: the three principal lines in the palmprint range are extended and auxiliary lines are drawn. The three principal lines are cut into two, then each principal line has two length features. The different positions and angles of principal lines influence the data significantly.
The elementary theory of extension is the extension theory. The pillars of extension theory are matter-element theory and extension set theory, using matter-element transformation to solve contradiction and incompatibility problems. The extension set uses extension model to handle subjective and objective contradiction problems of traditional mathematics and fuzzy mathematics. The range of fuzzy set is extended from
In the extension theory, elements of object consist of the name
Generally, an object has multiple characteristics, if an object
The traditional classical set uses 0 and 1 magnitudes concept to describe objects with or without some characteristic, whereas the fuzzy theory uses membership function to describe the fuzzy degree in fuzzy set range
If
The matter-element model should be built before recognition. The extension classical domain is set, and then the object
The matter-element model can be used for recognition, as shown in Figure
Flow chart of identification method.
Read the built matter-element model as
Set up neighborhood domain as
The neighborhood domain is the total range of whole characteristic set in all matter-element sets, the upper and lower limits of maximum classical domain are determined as (
Read data to be tested as (
Where
Calculate correlation functions of matter-elements.
In the extension theory, the classical domain
Extension correlation function.
Find out the maximum correlation function, that is, maximum correlation grade.
The recognition type is displayed when the maximum correlation grade is greater than or equal to threshold
During recognition, the recognition system searches for the maximum correlation grade in the optimal matter-element model according to the test matter-element and takes it as the identity of the person. There should be a threshold to review the maximum correlation grade, so as to avoid any identity outside the database entering the database.
As for the setting of optimal threshold, first, an initial threshold is set, and then the false rejection rate (FRR) and the false acceptance rate (FAR) values at this threshold are calculated. FRR is the recognition rate of system misidentifying a person inside database as one outside database; FAR is the recognition rate of system misidentifying a person outside database as one inside database. The sample of false rejection is set as
The threshold is adjusted slowly to observe the variance in FRR and FAR, as shown in Figure
FAR and FRR distribution in extension theory.
If data recognition is completed, stop; otherwise return to Step
Since the capture tool used in this paper is a charge-coupled device-webcam, which tends to be influenced by light, the capture structure is a semienclosed space.
For hand image capture, the hand is put in the semienclosed space with palm up, as shown in Figure
The semienclosed box.
In order to avoid the influence of ambient light, an LED lamp was fixed inside the semienclosed space to give fixed light. The interior was pasted with light reflecting paper, as shown in Figure
Inside semienclosed box.
Besides a webcam, the recognition system proposed in this study requires a computer with Visual Basic 6.0 program to process hand images, as shown in Figure
Man-machine interface of the proposed system.
In order to prove the recognition capability of the method proposed in this study, the experiment chose 25 males and 10 females as subjects, totaling 35 subjects, and 20 of them were present in the database. In addition, as the recognition algorithm of this experiment should run in two stages—training mode and recognition mode, the data of all the subjects were divided into training samples and recognition samples. The data of subjects captured in the recognition mode of Stage 1 were training samples, as the weight of each data was determined by recognition algorithm, so as to complete the optimum matter-element model. The recognition mode of Stage 2 compared the hand data outside the completed matter-element model with previously built matter-element model, to check whether the identity was inside the database, if yes, the identity of the subject could be indicated.
The images of each subject’s hand were captured at three different times for objectivity and reality, 5 images each time; thus, each subject had 15 images, 10 of them were training samples, and 5 were recognition samples. There were 250 images as training samples for building the matter-element model, and the rest of 275 images were used to test the recognition accuracy rate of this recognition algorithm, as shown in Table
Experimental data.
Database patterns | Not database patterns | |
---|---|---|
Number | 25 | 10 |
15 images each person | 375 images | 150 images |
for recognition/for training | 250 images for training/125 images for recognition | 150 images for recognition |
The traditional methods were compared with the proposed method, as shown in Table
Recognition results.
Recognition methods | Learning times | Learning accuracy | Testing accuracy |
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Proposed method | 0 | n/a | 91% |
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0 | n/a | 63% |
Backpropagation neural network (13-20-20) | 1000 | 89% | 87% |
Backpropagation neural network (13-22-20) | 1000 | 91% | 89% |
In addition, different recognition systems are compared in Table
Comparison among different recognition systems.
Price |
Accuracy | Speed (sec.) | Advantages | Disadvantages | |
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About |
91% | 5 s |
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About |
92% | 5 s |
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About |
98% | 15 s |
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About |
90% | 3 s |
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The proposed recognition system is consisted of a webcam and a semienclosed box for fixing light. Visual Basic 6.0 is used to process hand images and capture features for identification. First, when the webcam captures the palmprint features, the image should be preprocessed before feature extraction. As the image is inside the semienclosed box, it is free from ambient light interference. The user only needs to lay his hand open naturally for photographing. The experimental results showed that the accuracy rate of the extension recognition algorithm in this study is 91%, which is higher than traditional neural algorithm. This new approach merits more attention, because the low-cost device deserves serious consideration as a tool in palmar recognition problems. We hope this paper will lead to further investigation for industrial applications.