Palmprint Identification by Fourier Transform

Li, Wenxin; Zhang, David; Xu, Zhuoqun

doi:10.1142/s0218001402001757

Cited by 301 publications

(124 citation statements)

References 9 publications

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“…The literature [11] provided an updated survey of palmprint recognition methods, and presented a comparative study to evaluate the performance of the state-of-the-art palmprint recognition methods. According to [11], the proposed classification approach compares with those currently usually used approaches such as FCM [6], Median filter [9], Gabor [12] and Fourier transform [13]. The experimental results show that the average correct classification rates of these approaches are 95.57%, 94.11%, 93.24%, 92.45% and 88.17% in simulation of 500 times, respectively.…”

Section: Experiments and Analysismentioning

confidence: 98%

Target Image Classification through Encryption Algorithm Based on the Biological Features

Chen¹,

Wu²,

Yang³

2015

IJIS

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In order to effectively make biological image classification and identification, this paper studies the biological owned characteristics, gives an encryption algorithm, and presents a biological classification algorithm based on the encryption process. Through studying the composition characteristics of palm, this paper uses the biological classification algorithm to carry out the classification or recognition of palm, improves the accuracy and efficiency of the existing biological classification and recognition approaches, and compares it with existing main approaches of palm classification by experiments. Experimental results show that this classification approach has the better classification effect, the faster computing speed and the higher classification rate which is improved averagely by 1.46% than those of the main classification approaches.

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Section: Experiments and Analysismentioning

confidence: 98%

Target Image Classification through Encryption Algorithm Based on the Biological Features

Chen¹,

Wu²,

Yang³

2015

IJIS

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“…For extracting the central part, a coordination system should have to be established. There are several implementations including tangent [1], bisector [6,7] and finger based [8,9] to detect the key points between fingers.…”

Section: Related Workmentioning

confidence: 99%

Advanced Authentication Scheme using Multimodal Biometric Scheme

Mohan¹,

Ephin²

2013

IJCATR

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Fingerprint recognition has attracted various researchers and achieved great success. But, fingerprint alone may not be able to meet the increasing demand of high accuracy in today's biometric system. The purpose of our paper is to inspect whether the integration of palmprint and fingerprint biometric can achieve performance that may not be possible using a single biometric technology. Pre-processing is done for fingerprint and palmprint images separately in order to remove any noise. The next step is feature extraction. Minutiae algorithm is used for fingerprint feature extraction and Local Binary pattern for palmprint. Wavelet fusion is applied in order to fuse the extracted features and Support Vector Machine is used for matching. The main highlight of the project is multimodal biometrics which will give a better security and accuracy comparing to unimodel system.

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“…But they still have many disadvantages. For fingerprint recognition, only a small portion of mobile devices have a fingerprint sensor, and some people do not have clear fingerprints [1]. Face and voice recognition are sometimes not accurate and robust enough for personal recognition [1].…”

Section: Introductionmentioning

confidence: 99%

“…Various palmprint recognition approaches have been proposed in the literatures [1][2][3][4][5][6][7][8][9][10][11][12]. Almost all available algorithms involve thousands of large-size convolutions or filtering, i.e.…”

Section: Introductionmentioning

confidence: 99%

Embedded Palmprint Recognition System on Mobile Devices

Han¹,

Tan²,

Sun³

et al.

Advances in Biometrics

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Abstract. There are increasing requirements for mobile personal identification, e.g. to protect identity theft in wireless applications. Based on built-in cameras of mobile devices, palmprint images may be captured and analyzed for individual authentication. However, current available palmprint recognition methods are not suitable for real-time implementations due to the limited computational resources of handheld devices, such as PDA or mobile phones. To solve this problem, in this paper, we propose a sum-difference ordinal filter to extract discriminative features of palmprint using only +/-operations on image intensities. It takes less than 200 ms for our algorithm to verify the identity of a palmprint image on a HP iPAQ PDA, about 1/10 of state-of-the-art methods ' complexity, while this approach also achieves high accuracy on the PolyU palmprint database. Thanks to the efficient palmprint feature encoding scheme, we develop a real-time embedded palmprint recognition system, working on the HP PDA.

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Palmprint Identification by Fourier Transform

Cited by 301 publications

References 9 publications

Target Image Classification through Encryption Algorithm Based on the Biological Features

Target Image Classification through Encryption Algorithm Based on the Biological Features

Advanced Authentication Scheme using Multimodal Biometric Scheme

Embedded Palmprint Recognition System on Mobile Devices

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