Finger Vein Recognition Using Histogram of Competitive Gabor Responses

Lu, Yu; Yoon, Sook; Xie, Shan Juan; Yang, Jucheng; Wang, Zhihui; Park, Dong Sun

doi:10.1109/icpr.2014.309

Cited by 29 publications

(15 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Euclidean distance is a highly popular distance measurement method that measures the physical distance between two points in n-dimensional space using Equation (2). In biometrics, the Euclidean distance has also been successfully used in applications such as person recognition [25], finger-vein recognition [57], and face recognition [58]. To measure the verification performance, we first measured the distance between image pairs.…”

Section: Resultsmentioning

confidence: 99%

“…The reason for the use of Euclidean distance is that this similarity measurement method has been used in previous studies [25,57,58]. In this section, we will exploit a new kind of distance measurement method for similarity evaluation based on correlation measurement.…”

Section: Resultsmentioning

confidence: 99%

“…For similarity measurement, as mentioned in our paper, the Euclidian and correlation distance have been widely used by previous researches [25,57,58,59]. Beside these two measurement methods, other methods can be used.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

427

159

View full text Add to dashboard Cite

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

427

159

View full text Add to dashboard Cite

show abstract

“…Xie et al [17] proposed a method combining symmetric Gabor filter with a steering filter to extract the finger-vein image. Lu et al [18] proposed a feature extraction method based on Gabor-corresponding histograms. Yang et al proposed the Gabor+Tri-branch structure [19] and point group method combined with a Gabor filter (PG-Gabor) [20] to utilize the vein point and non-vein point.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning Gabor Filter for Finger-Vein Recognition

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Presently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein. We combine convolutional neural networks with a Gabor filter to calculate the gradient of the Gabor-filter parameters, based on the objective function, and to then optimize its parameters via back-propagation. The parameter θ of Gabor filter can be trained to the same angle as the vein texture of finger vein image. The parameter σ of Gabor filter has a certain relation with λ, and the parameter λ of Gabor filter can converge to the optimal value well. Using this method, we not only select appropriate and effective Gabor filter parameters to design the filter banks, we also consider the relationship between those parameters. Finally, we perform experiments on four public finger-vein datasets. Experimental results demonstrate that our method outperforms state-ofthe-art methods in finger-vein classification.

show abstract

“…At present, the commercialized and extensively used biometrics methods are finger vein recognition at an entry/exit door, face recognition of Google Android phones, fingerprint recognition of Apple iPhone and voice recognition. Finger vein recognition [19,20]is one of the most well-known biometrics refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals and it has been proved to be an effective biometric for personal identification in recent years. This method is getting popular because it is not necessary to carry a plastic card such as IDcard and there is very little chance of being a victim of theft.…”

Section: Introductionmentioning

confidence: 99%