Research On Palmprint Recognition Methods Of Non-ideal Conditions

In the modern society where the information interaction is becoming increasinglyfrequent, the identity authentication seems more and more important. It has raised greatattention and is being used extensively in the sectors of e-commerce, public security andcommercial finance. Compared with traditional personal identification methods, biometricshas higher reliability and simplicity. It’s the front direction of pattern recognition, imageprocessing, artificial intelligence or other fields, and has good and broad prospects fordevelopment. Palmprint recognition is a kind of new technology in biometrics field,compared with other biometrics technology, palmprint recognition has many uniqueadvantages. Besides, palmprint can be obtained by non-contact imaging method, which isnon-invasive with high degree of public acceptance. Therefore, palmprint recognition hasalways been one of the researching hotspots in the biometrics field.Traditional contact acquisition device has poor portability with high cost, and is easyto cause the wear of the equipment and the spread of the disease. All these make thenon-contact acquisition has gradually become the mainstream method for palmprint imageobtaining. However, the differences of the personal placing habits and feelings may resultin the non-ideal status with deformed or blurred palmprint images during the non-contactacquisition, which will undoubtedly reduce the performance of the palmprint recognitionsystem. In view of the above problems in the practical application, this thesis takes thepalmprint recognition of non-ideal conditions as the research content, the stable featuremethod as the main idea, and the self-built palmprint image database as the experimentalbasis, proposes the solutions to the deformed and blurred palmprint recognition problems.Specifically, the main work of this thesis is as follows:(1) Low restriction of the non-contact palmprint collection may cause different palmplacing gestures and different distance between the palm and the camera, these may resultin palm image deformation. Considering the non-rigid characteristic of human hands, anormalization model based on Demons non-rigid registration algorithm is proposed fromthe angle of preprocessing to better enhance the similarity between the deformed image and the standard image, and compensates the shortages of the traditional rigid methodwhich is not very effective. First, the improved Demons algorithm is used to normalize thedeformed palmprint; next, some technical indicators are employed to evaluate the results.The experimental results demonstrate that the indicators are better than the traditional rigidregistration method in randomly selected image sequence. This suggests that the similaritybetween the deformed palmprint and standard palmprint has been effectively improved,and the proposed methods have created favorable conditions for subsequent featureextraction and recognition.(2) In order to improve the limited effect of Scale Invariant Feature Transform (SIFT)about affine transformation caused by no parallel of the palm plane and the sensor planeduring contactless palmprint acquisition, a better method of palmprint recognition whichbased on Affine Scale Invariant Feature Transform (ASIFT) is proposed from the angle offeature extraction without deformation correction. Firstly, a model of affine deformation ofpalm is given, then the latitude and longitude of camera axis are simulated, and imagefeatures in the affine space are extracted. Based on the practical application environment,the deformed pamlprint database is established for the performance tests. Compared withthe SIFT and other typical palmprint recognition methods, the experimental results showthat the ASIFT can achieve the best performance when it’s employed to resist the affinedeformation of palmprint. In conclusion, the proposed algorithm can successfully solve thedeformation problem of palmprint, and it’s more effective, with superiority, robustness andstability.(3) In view of the practical problem of blurred image caused by defocus status fornon-contact palmprint collection which may reduce the performance of the recognitionsystem, a novel solution called stable features (SF) theory is proposed based onestablishing the blurred model and analyzing the blur mechanism, and a blurred palmprintrecognition method based on DCT and block energy of principal lines (PLBE) is presentedfurther. As the stable features, the low frequency coefficients are extracted by discretecosine transform(DCT) in the frequency domain, and the principal lines are extracted bythe improved local gray minimum method in the spatial domain. Thereafter the blockmethod is used for calculating principal lines energy to form the feature vectors. Then thestable features in the frequency and spatial domain are fused. Finally, the Euclideandistance between vectors is used for classification and identification. The experimentsbased on the self-made blurred palmprint database show that the proposed algorithm (DCT+PLBE) can get the best performance compared with no fusion and other typicalidentification methods, and this means it is an effective and superior approach which cansolve the problem of blurred palmprint recognition.(4) The defocus status caused by non-contact collection for palmprint will lead toimage blur, and result in the poor recognition performance of the identification system. Inorder to solve this practical problem, a novel solution is proposed based on the stablefeatures (SF) theory. Firstly, the laplacian smoothing transform (LST) is employed toextract the low-frequency coefficients of the blurred palmprint as the stable features.Secondly, the hand geometric features, namely the relative lengths and widths of thefingers are also extracted as the measurement features. Then the LST features andgeometric features are fused to constitute the new vectors. Finally, the Euclidean distancebetween the vectors is used for matching and classification. The experiments based on theself-made blurred palmprint database show, compared with no fusion and other typicalidentification methods, the proposed algorithm (LST+HGF) can get the best performance.This demonstrates that the algorithm is an effective and superior approach which canimprove the performance of blurred palmprint recognition system.