Camera Based Palmprint Recognition

 Chhaya Methani

There was a time when biometrics was looked upon as the science of the future. It has featured prominently in various science fiction movies as an advanced security measure used to safeguard important documents, buildings etc. With the help of fast paced technological innovation, today, this is not far from reality. Biometrics is increasingly being used for secure authentication of individuals and making its presence felt in our lives. It uses an individual’s physical or behavioral traits to identify them. The decision of which biometric is to be used for a particular application is a complex function of the people’s security needs, ease of use and size of the enterprise. People can now see biometrics based security checks at airports that use iris and hand geometry based authentication and at ATMs using fingerprint and hand veins for authentication.

The stage is now set for the use of biometric recognition in commercially viable civilian applications. Mobile devices that can connect to computational servers and laptops can benefit from this technology in making our systems more secure. Most of these devices now come with attached commodity cameras which can be used for biometric image capture. An easy-to-capture biometric modality that could work well with a commodity camera is palmprint. It has coarse lines which can be easily detected using a low resolution camera and it is easy to present due to the free mobility of our palm. On most surveys, hand as a biometric modality rates high on user acceptance. For these reasons, palmprint would be an ideal choice for recognition using commodity cameras.

Consumer devices, however, employ image capture in an open environment as opposed to the controlled environment preferred for biometric data capture by state-of-art designs. For palmprint image capture, a semi-closed environment is created using a box-like setup having an illumination source on top, resulting in clean images with pre-fixed pose and illumination settings. Inspite of the resulting high accuracy, it is not practically feasible to provide a semi closed setup with a laptop or a mobile device where people are using their systems everyday and want fast access on a regular basis. The unrestricted imaging associated with mobile cameras results in huge intra class variations of palm. The performance of existing techniques for palmprint authentication fall considerably, when the camera is not aligned with the surface of the palm. The problems arise primarily due to variations in appearance introduced due to varying pose, but is compounded by specularity of the skin and blur due to motion and focus. Hence, we need novel recognition methods for images captured in an unconstrained environment with the hand presented to the system in an unsupervised manner.

In this thesis, we propose the design of a biometric system used for unconstrained and unsupervised camera based palmprint recognition system. We present a new pose transformation algorithm that can identify individuals even after seeing the hand being presented in a pose different than that stored in the database. The method can robustly estimate and correct variations in pose, and compute a similarity measure between the corrected test image and a reference image. The method is able to correct for pose variation even in degraded images having variable illumination.

However, another challenge surfaces during matching of the palm wherein bad line visibility worsens recognition performance. Hence, we propose another algorithm that separates out the original palm lines from the false line like impressions created due to illumination, contrast variations and loose skin. Even minor changes in pose of the palm can induce significant changes in the visibility of the lines. We turn this property to our advantage by capturing a short video, where the natural palm motion induces minor pose variations, providing additional texture information. This is important for improved matching of images. We propose a method to register multiple frames of the video without requiring correspondence, while being efficient.

Since, this is the first attempt at creating an unconstrained palmprint recognition system, we created two in-house databases to model the pose and illumination variations related to the palm image capture process used by us. The first database contains images of 100 users, having 5 images each having variable poses. The second database captures 6 videos each for 100 subjects captured using a regular web camera. Both the datasets have been captured under natural illumination conditions. Experimental results on the first dataset using the pose correction algorithm shows a reduction in Equal Error Rate from 22.4% to 8.7%. Through an independent experiment performed on the video palm database, we observed that the use of multiple frames reduces the error rate from 12.75% to 4:7%. We also propose a method for detection of poor quality samples due to specularities and motion blur, which further reduces the EER to 1.8%.

Related Publications

• Chhaya Methani and Anoop M. Namboodiri - Pose Invariant Palmprint Recognition Proceedings of the 3rd International Conference on Biometrics (ICB 2009), pp. 577-586, June . 2-5, 2009, Alghero, Italy. [PDF]

• Chhaya Methani, Anoop Namboodiri - Video Based Palmprint Recognition, Proceedings of the 20th International Conference on pattern Recognition (ICPR 2010), August, 2010, Istanbul, Turkey [PDF] 

Demonstrations

• Presentation at ICB (here...)
• Poster hat ICPR (here...)

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An Approximate Nearest Neighbor Retrieval Scheme for Computationally Intensive Distance Measures

Pratyush Bhatt (homepage)

Nearest neighbor retrieval can be defined as the task of finding the objects that are most similar to a query from a given a database of objects. It find its application in areas ranging from medical domain, financial sector, computer vision, computational sciences, computational geometry, information retrieval, etc. With the expansion of internet, the amount of digitized data is increasing by leaps and bounds. Retrieval of nearest nearest neighbors accurately and efficiently becomes challenging in such a scenario as the database contain a large number of objects. The problem gets worsen when the underlying distance measure used to compute [dis]similarity is computationally expensive. In such a scenario, sequential scan of data would take a lot of time which is the biggest problem for any online retrieval system. For example in biometric authentication systems, a particular person biometric template is compared against all the registered samples in a database to identify the person. This process can be extremely time consuming in large databases even if the matching algorithm is extremely fast. For example, to do background check of a person who is crossing the border using the complete IAFIS,(a biometric person identification system at the U.S. border crossings), requires around 55 million comparisons. Even with the state of the art matching algorithms and computing facilities, this would take close to 10 minutes, which is not practical considering the millions of people who cross the border every month. Even for criminal investigations, it is desirable to get a quick and approximate search done immediately rather than the typical turn-around time of a few days for a search. This thesis proposes a novel method for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed technique is domain-independent, and can be applied in arbitrary spaces, including non- Euclidean and non-metric spaces. The main contributions of our work are :

1. A representation scheme for objects in a dataset that allows for fast retrieval of approximate nearest neighbors in non-euclidean space. The approach named Hierarchical LocalMaps (HLM),make use of manifold learning techniques to compute linear approximation of local neighborhoods.

2. Search mechanism combined with filter and refine approach is proposed that minimizes the number of exact distance computations for computationally expensive distance measure.

3. Study performance of our scheme on biometric data and study the parameters affecting its performance.

Results of k-nearest neighbor retrieval as well as classification results on UNIPEN dataset shows the advantages of using HLM over state-of-the-art approximate nearest neighbor retrieval algorithms. Classification result on CASIA iris dataset by using average gabor response for a block as the feature vector along with Euclidean distance as the soft biometric measure in conjugation with Daugman feature vector and hamming distance as the hard biometric shows the advantage of using a softer metric over a hard metric for indexing.

Related Publications

• Pratyush Bhatt and Anoop Namboodiri - Hierarchical Local Maps for Robust Approximate Nearest Neighbour Computation Proceedings of the 7th International Conference on Advances in Pattern Recognition (ICAPR 2009), Feb. 4-6, 2009, Kolkotta, India. [PDF]

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Scene Interpretation in Images and Videos.

Chetan Jakkoju (homepage)

Scene interpretation is a fundamental task in both computer vision and robotic systems. We deal with two important aspects of scene interpretation, they are scene reconstruction and scene recognition. Scene reconstruction is determining 3D positions of world points and retrieving camera poses from images. It has several applications such as virtual building editing in computer aided architecture, video augmentation in film industry and planning and navigation in mobile robotics. Among several approaches to modeling the scene, we deal with piecewise planar modeling due to several advantages: Man-made environments are often piece-wise-planar, planar modeling has compact representation and this can be easily modified. We propose a convex optimization based, approach for piecewise planar reconstruction. We show that the task of reconstructing a piece-wise planar environment can be set in an L8 based Homographic framework that iteratively computes scene plane and camera pose parameters. Instead of image points, the algorithm optimizes over inter-image homographies. The resultant objective function is minimized using Second Order Cone Programming algorithms. Apart from showing the convergence of the algorithm, we also empirically verify its robustness to error in initialization through various experiments on synthetic and real data. We intend this algorithm to be in between initialization approaches like decomposition methods and iterative non-linear minimization methods like Bundle Adjustment.

Scene recognition in robotics, specifically terrain scene recognition is one of the fundamental tasks of autonomous navigation. Navigable terrains are examples of planar scenes. The goal of terrain recognition is to recognize various terrains that occur in urban and rural environments in an automated fashion. It has applications in various domains such as advanced driver assistance systems, remote sensing, etc. Various sensing modalities such as ladars, lasers, accelerometers, stereo cameras, omni-directional cameras or combination of them are used in literature. This thesis attacks the problem of scene interpretation using a single camera. This investigation is especially crucial since cameras are relatively low in cost, consume low power, light weight and have the potential to provide very rich information about the environment. Recent advances in computer vision, machine learning and improvements in hardware capabilities have greatly increased the scope of monocular camera, even in unstructured and real world environments. In this thesis, we start with empirical study of promising color, texture and their combination with classifiers such as Support Vector Machines (SVM) and Random Forests. We present comparison across features and classifiers. Then we present a monocular camera based terrain recognition scheme called Partition based classifier. The uniqueness of the proposed scheme is that it inherently incorporates spatial smoothness while segmenting an image, without the requirement of any additional post-processing. The algorithm is fast because it is build on top of a Random Forest classifier. The efficacy of the proposed solution can be seen as we reach low error rates on both our dataset and other publicly available datasets.

Further partition classifier is extended to be online and adaptive. The new scheme consists of two underlying classifiers. One of which is learnt over bootstrapped or offline dataset, the second is another classifier that adapts to changes on the fly. Posterior probabilities of both the static and online classifiers are fused to assign the eventual label for the online image data. The online classifier learns at frequent intervals of time through a sparse and stable set of tracked patches, which makes it lightweight and real-time friendly. The learning which is acuted at frequent intervals during the sojourn significantly improves the performance of the classifier vis-a-vis a scheme that only uses the classifier learnt offline. The method finds immediate applications for outdoor autonomous driving where the classifier needs to be updated frequently based on what shows up recently on the terrain and without largely deviating from those learnt offline.

Related Publications

• Chetan J, Madhava Krishna and C. V. Jawahar - Fast and Spatially-smooth Terrain Classification using Monocular Camera Proceedings of 20th International Conference on Pattern Recognition (ICPR'10),23-26 Aug. 2010, Istanbul, Turkey. [PDF]

• Chetan J., Madhava Krishna and C. V. Jawahar - An Adaptive Outdoor Terrain Classification Methodology using Monocular Camera In proceedings of International Conference on Intelligent Robots and Systems. (IROS 2010 )
• Visesh Chari, Anil Nelakanti, Chetan Jakkoju and C. V. Jawahar - Piecewise Planar Reconstruction using Convex Optimization In proceedings of Asian Conference on Computer Vision (ACCV 2009).

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Large Scale Character Classification

Neeba N.V (homepage)

Large scale pattern recognition systems are necessary in many real life problems like object recognition, bio-informatics, character recognition, biometrics and data-mining. This thesis focuses on pattern classification issues associated with character recognition, with special emphasis on Malayalam. We propose an architecture for the character classification, and proves the utility of the the proposed method by validating on a large dataset. The challenges in this work includes, (i) Classification in presence of large number of classes (ii) Efficient implementation of effective large scale classification (iii) Performance analysis and learning in large data sets (of Millions of examples). Throughout this work, we use examples of characters (and symbols) extracted from real-life Malayalam document images. Developing annotated data set at the symbol level from a coarse (say word-level) annotated data is addressed first with the help of a dynamic programming based algorithm. Algorithm is then generalized to handle the popular degradations in the form cuts, merges and other artifacts. As a byproduct this algorithms allows to quantitatively estimate the quality of the books, documents and words. The dynamic programming based algorithm align the text (in UNICODE) and image (in Pixels). This helps in developing a large data set which could help in conducting large scale character classification experiments.

We then conduct an empirical study of classifiers and feature combination to study their suitability to the problem of character classification. The scope of this study include (a) applicability of a spectrum of classifiers and features (b)scalability of classifiers (c) sensitivity of features to degradation (d) generalization across fonts and (e) applicability across scripts. It may be noted that all these aspects are important to solve the character classification problem. Our empirical studies and theoretical results provide convincing evidences to support the utility of SVM (multiple pair-wise) classifiers for solving the problem. However, a direct use of multiple SVM classifiers has certain disadvantages: (i) since there are nC2 pairwise classifiers, storage and computational complexity of the final classifier becomes high for many practical applications. (ii) they directly provide a class label and fail to provide an estimate of the posterior probability. We address these issues by efficiently designing a Decision Directed Acyclic Graph (DDAG) classifier and using the appropriate feature space. We also propose efficient methods to minimize the storage complexity of support vectors for the classification purpose. We also extend our algebraic simplification method for simplifying hierarchical classifier solutions.We use SVM pair-wise classifiers with DDAG architecture for classification. We use linear kernel for SVM, considering the fact that most of the classes in a large class problem are linearly separable.

We carried out our classification experiments on a huge data set, with more than 200 classes and 50 million examples, collected from 12 scanned Malayalam books. Based on the number of cuts, merges detected, the quality definitions are imposed on the document image pages. The experiments are conducted on pages with various quality. We could achieve a reasonably high accuracy on all the data considered. We do an extensive evaluation of the performance on this data set which is more than 2000 pages.

In presence of large and diverse collection of examples, it becomes important to continously learn and adapt. Such an approach could be more significant while recognizing books. We extend our classifier sysyem to continuously improve the performance by providing feedback and retraining the classifier. This thesis focuses on pattern classification issues associated with character recognition, with special emphasis on Malayalam. We propose an architecture for the character classification, and proves the utility of the the proposed method by validating on a large dataset. The challenges in this work includes, (i) Classification in presence of large number of classes (ii) Efficient implementation of effective large scale classification (iii) Performance analysis and learning in large data sets (of Millions of examples).

To summarize, major contributions of this work are:

1. A highly script independent dynamic programming (DP) based method to build large dataset for testing and training character recognition systems.
2. Empirical studies on large dataset of various Indian languages to evaluate the performance of state of the art classifiers and features on large datasets.
3. A hierarchical method to improve the computational complexity of SVM classifier for large class problems.
4. An efficient design and implementation of SVM classifier to effectively handle large class problems. The classifier module has employed for a OCR system for Malayalam.
5. The performance evaluations of the above mentioned methods on a large dataset. We tested on a large dataset of twelve Malayalam books, which is more than 2000 document pages.
6. A novel system for adapting a classifier for recognizing symbols in a book.

(more...)

Related Publications

• Neeba N.V., and C. V. Jawahar - Empirical Evaluation of Character Classification Schemes Proceedings of the 7th International Conference on Advances in Pattern Recognition (ICAPR 2009), Feb . 4-6, 2009, Kolkotta, India. [PDF]

• Ilayaraja Prabhakaran, Neeba N.V., and C.V. Jawahar - Efficient Implementation of SVM for Large Class Problems Proc. of the 19th International Conferenc eon Pattern Recognition(ICPR 08), Dec. 8-11,2008, Florida, USA. [PDF]
  

• Neeba N.V., and C. V. Jawahar - Recognition of Books by Verification and Retraining Proc. of the 19th International Conference on Pattern Recognition(ICPR 08), Dec. 8-11,2008, Florida, USA. [PDF]

Book Chapter

• N.V. Neeba, Anoop Namboodiri, C.V. Jawahar and P. J. Narayanan - Recogniton of Malayalam Documents, in Guide To Ocr For Indic Scripts: Part 1: Document Recognition And Retrieval - 2010.

Demonstrations

• Presentation at ICAPR-2009 Presentation at ICAPR-2009
• Poster at ICPR-2008 (SVM)
• Poster at ICPR-2008 (Recognition)

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