Using Webcam to Enhance Fingerprint

Recognition

Bibek Behera, Akhil Lalwani, and Avinash Awate

Indian Institute of Technology, BombayPowai, Mumbai-400076, India

bibek@cse.iitb.ac.in,

lalwani.akhil@gmail.com,

awate_avinash@yahoo.com

http://www.cse.iitb.ac.in

Abstract. Fingerprint recognition has always played an important rolein biometrics. Presently, biometric devices come with scanners that takeimages in a controlled manner. This paper studies how webcams canbe used to take images in more or less uncontrolled manner to produceimages that can be used for fingerprint matching. The idea is to takephoto of a finger(preferably thumb) casually from a webcam either usinga mobile device or tablet. The image is then preprocessed using tech-niques such as gamma normalisation, HDR (High Dynamic Range) ton-ing and other tools from Photoshop softwares. The preprocessed image isthen fed to the fingerprint enhancement algorithm. Our experimentationshows that inspite of lack of light or high quality cameras, our system isrobust to change in brightness or quality of cameras.

Keywords: Gamma normalisation, HDR toning, ridge, local adjust-ment, fingerprint, webcam.

1 Introduction

This paper describes a simple webcam based approach for enhancing images forautomatic fingerprint identification system (AFIS). The idea of a webcam basedAFIS comes in the backdrop of conducting technical courses all over India across300 institutes as a part of IIT-Bombay edX distant learning educational pro-gram. This software will be installed in local servers at distant learning centres.The software can be used for authorisation during test and quizzes to preventunauthorised persons from taking test for others. These days biometric devicesare used everywhere for fingerprint recognition. These devices have hardware aswell as software componenets that could cost around Rs. 22,000 or 300 e. In-stalling these devices all over India would be an expensive process that severelylimits the outreach of our program. We need to ensure that our webcam basedapproach meets the quality standards of existing biometric system. So in thispaper, we have compared our techniques with Peter Kovesi’s code which looselyfollows the approach presented by Hong et al. [2], which is the present state ofthe art for fingerprint recognition.

F.J. Perales and J. Santos-Victor (Eds.): AMDO 2014, LNCS 8563, pp. 51–60, 2014.c© Springer International Publishing Switzerland 2014

52 B. Behera, A. Lalwani, and A. Awate

This work is centered around various pre-processing techniques such as HDRtoning. All these techniques are well-known in Photoshop R©1 world. They havebeen used to enhance quality of images such as human faces, natural scenery, etc.These methods can bring out darker aspects of images and make them visible.Sometimes when we take images in natural light, often the picture has patches ofhighlights and shadows. These regions are not suitable for fingerprint matchingusing webcam. In these cases, we need some advanced image processing tech-niques like gamma normalisation, HDR toning that can normalise the highlightsand shadows of images. Highlights are over-illuminated regions and shadows areunlighted regions.

A very simple yet effective technique is gamma normalisation that darkensdarker parts and lightens lighter parts. This technique has been shown by Islamet al. [3] to bring out ridges in a fingerprint to a level of clarity sufficient for finger-print identification and has also improved the accuracy of fingerprint matching.Despite this fact, gamma resolution fails over the shadowed regions because theyare darker but the requirement is extraction of ridges from darker regions. Thisleads to failure of such system in absence of enough light. Moreover even if wechange the gamma value to reverse the effect of contrast i.e. make dark partslighter and lighter parts darker, again we lose information from the lighter parts.Also the darker parts are not enhanced to a huge extent. This makes gammanormalisation an ineffective technique and there is scope for better techniquesfor producing better quality images.

Other works include Hong et al. [2] which describes the image enhancementalgorithm on ridge extraction. We describe a novel approach that uses HDR ton-ing to preprocess images before using the ridge extraction algorithm. Techniqueslike HDR toning work way better in adding light to darkened regions. In HDRtoning, there are four techniques. In this paper, we investigate the role of eachtechnique.

The rest of the paper is divided into 4 sections. In section 2, we describe HDRtoning, various modes of HDR toning and comparison of these modes. In section3, we discuss the algorithm used to develop the skeleton of fingerprint from theimage of the finger. Section 4 describes our details of our experimental set-upalong with results. Finally we conclude in section 5.

2 HDR-Toning

Before knowing about HDR toning, we need to understand the background andpeculiarities of HDR.

2.1 Background of HDR

HDR means high dynamic range i.e. 32 bit images. Dynamic range in digitalphotography means ratio between maximum and minimum measurable intensity.

1 Product is copyright of Adobe Systems.

Using Webcam to Enhance Fingerprint Recognition 53

Every device has its own fixed range. For example in print nothing can becomebrighter than the paper itself or in display nothing can become brighter than themaximum intensity pixel. Similarly our eyes also have their own dynamic range.8 bit or 16 bit images are classified into LDR (Low dynamic range). HDR imagesuse these extra bits to create an arbitrarily large scale to fit the requirements ofany kind of scene.

2.2 Peculiarities of HDR

HDR empowers the photographer to enhance the range of tonal detail betterthan that provided by any camera. Merge to HDR is an option in Photoshop thatintegrates a number of bracketed exposures to one single image, which magicallyhas the tonal detail of the entire variation of exposure2. But as we know there isnothing free of cost, expansion in tonal range comes at the expense of decreasedcontrast in some tones.

2.3 HDR Toning - An Understanding

HDR toning transforms 8 bit images to 32 bit images, processes it and thenbrings the pixel values back to 8 bit range. It is like stretching an image andthen collapsing the image to produce a super clear image. Fig. 1 is the inputimage taken by a webcam using LED lights embedded in the webcam.

(a) (b)

Fig. 1. (a) Image taken from a 5MP webcam (b) Skeleton of fingerprint

2 http://www.cambridgeincolour.com/tutorials/high-dynamic-range.htm

54 B. Behera, A. Lalwani, and A. Awate

As we can see this image has shadows on the left side because the source of lightwas placed towards the right. In such cases the fingerprint skeleton is totallydarkened in the shadowed region as can be seen in Fig. 1. The skeleton is obtainedusing Peter Kovesi’s code 3 which was rewritten by us in Python. Once we haveobtained the skeleton, it is forwarded to the matching algorithm [2]. But ifthe image has darker segments or any relevant information is missing in theoriginal image, the algorithm described by Hong et.al [2] is unable to enhancethose segments. Techniques like HDR toning can introduce light in these darkerregions and hence come handy to discover hidden ridges as we will show in thispaper.

2.4 Various Modes of HDR

HDR toning has four modes of operation.

1. Exposure and Gamma2. Highlight Compression3. Histogram equalisation4. Local adaptation

Fig. 2 shows all the images obtained with these modes.

1. Exposure and Gamma: This mode allows us to control the exposure andgamma which are pictorially equivalent to brightness and contrast adjust-ment, respectively.

2. Highlight Compression: This method attempts to reduce contrast in the high-light portions of the image to get it to fit within a standard 16-bit space. Ithas no controls, functioning completely without user intervention. 4

3. Histogram equalisation: Histogram equalisation is a method of image pro-cessing of contrast adjustment using image’s histogram. One of the problemsfaced while taking images is unequal distribution of intensities. Some por-tions of the images are bright and some are darkened. Histogram equalisationredistributes the HDR histogram into the contrast range of 8 bit or 16 bitimage. This mode makes use of a custom tonal curve which spreads outhistogram peaks so that histogram becomes flat. An image with several rel-atively narrow peaks is more suitable for this operation.

4. Local adaptation: This mode is more flexible and one of utmost utility tophotographers. In contrast to above methods, this one modifies here theamount of brightness or darkness based on pixels surrounding it i.e. local-ity based enhancement. This creates an illusion that the image has morecontrast, which is of maximum requirement in contrast-impoverished HDRimages.

3 http://www.csse.uwa.edu.au/~pk4 http://www.earthboundlight.com/phototips/photoshop-cs2-hdr-32bit.html

Using Webcam to Enhance Fingerprint Recognition 55

(a) (b) (c)

(d) (e)

Fig. 2. (a) Image taken from a 5MP webcam with an LED torch (b) Image modifiedusing Exposure and Gamma normalisation (c) Highlight compression (d) Histogramequalisation (e) Local adjustment

We examined image as shown in Fig. 2 using an LED torch and noted the ridgereadings in various modes as mentioned above and depicted in Table 1.These images showed that the best reading was observed when local adaptionmode is used.

3 Algorithm

The algorithm is a step by step process to convert the fingerprint image into abinary image of ridges. The steps are laid down as follows:-

1. Local adaptation: This is a pre-processing step (as described in section 2.4,4)that improves the quality of image since it recovers maximum ridges.

56 B. Behera, A. Lalwani, and A. Awate

Table 1. Number of pixels recovered by varying the modes of HDR toning

Modes Number of pixels recovered

Original image 235601Exposure and gamma 221009Highlight Compression 257105Equalize histogram 249169Local adaptation 275537

2. Ridge segmentation: This module recognizes ridge areas of a fingerprint im-age and returns a mask identifying this region. It also normalises the intensityvalues of the image so that ridge regions have zero mean, unit standard de-viation.

3. Ridge orientation: This function estimates the local orientation of ridges ina fingerprint. The widely used gradient-based method is employed whichmakes use of the fact that orientation vector is orthogonal to the gradient[5,7,9]. The image is segmented into blocks and gradient is estimated inx and y direction for every pixel. The orientation matrix is calculated foreach block by taking the average of vectors orthogonal to the gradient pixels.

4. Ridge Frequency: This process calculates the fingerprint ridge frequencyacross a block of the fingerprint image. Firstly, the image is divided intoblocks and an orientation window is calculated along each block. The ridgesand valleys in the oriented window are used to construct an x-signature sig-nal. The x-signature is a projection of intensity of each pixel orthogonal tothe orientation window. The projection is a sinusoidal wave and the fre-quency of the wave gives the ridge frequency in that block.

5. Ridge Filter : This function enhances ridges in the fingerprint image via ori-ented filters. This function makes use of Gabor filters for enhancement whichhave found utility in fingerprint matching [6,8] and fingerprint classification[4]. Gabor filters are bandpass filters that are sensitive to frequency and ori-entation [1]. The fingerprints have a useful characteristic of well defined ridgeorientation and ridge frequency. When the Gabor filter is applied on eachpixel, it enhances the ridges oriented in the direction of local orientation anddiminishes anything oriented otherwise. The filter thus enhance ridges andblurs the background, thus cancelling out noise as described by Thai [10].

All these steps have been shown in Fig. 3.

4 Experimental Details

The aim of this experiment is to find the variation in quality of image enhance-ment algorithm by varying the light intensity. For this experiment we have used

Using Webcam to Enhance Fingerprint Recognition 57

(a) (b)

(c) (d) (e)

(f) (g) (h)

Fig. 3. (a) Original image (b) Preprocessed image with local adjustment (c) Mask afterridge segmentation (d) Ridge orientation (e) Ridge frequency (f) Ridge filter (g) Binaryimage (h) Final image

a 5MP webcam. The images have been taken in a dark room using a 6 LED highintensity torch.

4.1 Method Proposed

We have laid down the steps used to carry out these experiments.

1. We use a webcam W (having reasonably good resolution, say 1.2 MP) andfix it on table top.

2. Now we place the torch T parallel to the webcam and mark its distance dfrom the source S as shown in Fig. 4

3. Person X places his thumb on S, we record the image of thumb I1.4. Now we vary the position of T to a new value d+ y metres from S and take

image I2. The position of W is unchanged throughout the experiment.

58 B. Behera, A. Lalwani, and A. Awate

Fig. 4. Framework for varying intensity using LED torch and webcam

5. The position of T is varied n times. So distance values are d, d + y, d +2y, . . . , d+ (n− 1)y.

We repeat this experiment over four modes of HDR toning section 2.4. Each cellof Table 2 records the pixels discovered by these methods.We have collected fingerprints of 5 different persons and tabulated the result inTable 2. We have also graphically represented number of pixels recovered withdistance for three persons from a 5 MP pixel webcam in Fig. 5.

Fig. 5. 1. Number of pixels recovered by varying distance of source S from torch T ondifferent person with 5MP camera

Using Webcam to Enhance Fingerprint Recognition 59

Table 2. Number of pixels recovered by varying distance of source S from torch T ondifferent modes available with HDR toning for image I1 and 5 MP camera

Image I1,Webcam 5 MP Distance of source S from torch T

Methods d d+ y d+ 2y d+ 3y d+ 4y

No HDR 414464 570368 514816 515840 433664

Gamma Normalisation 627712 626432 674816 674816 706304

Highlight Compression 231936 462336 423936 423936 488448

Histogram Equalisation 1365760 1645824 586240 721408 1213440

Local Adaptation 4876544 4893184 4622592 4863744 4824320

5 Conclusion

Our experiments have shown that the quality of image is not lowered with vari-ation of light as the number of pixels recovered have not varied a lot throughoutour experiments. Thus our pre-processing technique makes the system robustto changes in amount of light intensity and can be employed in the fingerprintrecognition system. We are presently working on implementing our system forreal time identification systems, for the applications mentioned earlier.

Acknowledgements. The authors would like to thank IIT-Bombay MOOC (anonline learning initiative sponsored by NMEICT 5) for which this webcam basedfingerprint system is being developed. It is also expected to be deployed in futureversions of India’s indigenous low-cost tablet ‘Aakash’. Special thanks to Prof.D.B.Phatak (CSE Dept., IIT-Bombay), who is the principal investigator of thisproject, for his support.

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