Murat Ekinci

Palmprint Recognition by Applying Wavelet-Based Kernel PCA

This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classiffer. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.

Palmprint Recognition by Applying Wavelet Subband Representation and Kernel PCA

This paper presents a novel Daubechies-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. The palmprint is first transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, weighted Euclidean linear distance based NN classifier and support vector machine (SVM) are comparatively performed for similarity measurement. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.

Background Estimation Based People Detection and Tracking for Video Surveillance

This paper presents a real-time background estimation and maintenance based people tracking technique in an indoor and an outdoor environments for visual surveillance system. In order to detect foreground objects, first, background scene model is statistically learned using the redundancy of the pixel intensity values during learning stage, even the background is not completely stationary. A background maintenance model is also proposed for preventing some kind of falsies, such as, illumination changes, or physical changes. And then for people detection, candidate foreground regions are detected using thresholding, noise cleaning and their boundaries extracted using morphological filters. From these, a body posture is estimated depending on skeleton of the regions. Finally, the trajectory of the people in motion is implemented for analyzing the people actions tracked in the video sequences. Experimental results demonstrate robustness and real-time performance of the algorithm.

AAM-based palm segmentation in unrestricted backgrounds and various postures for palmprint recognition

Abstract In this paper, the AAM method with novel palm model is proposed for robust palm segmentation. The main advantages of this approach are the ability of efficient palm segmentation on the cluttered backgrounds and making a decision on whether the object in the scene is a palm with high accuracy. Especially, the proposed palm model eliminates the requirement that the whole hand image has to appear in the scene. The performance of the method is measured with two metrics which give more meaningful and quantitative results: the modified point-to-curve distance and a novel margin width suggested in this work. Furthermore, a novel device which performs the online palm image acquisition without any restriction has been developed. Experimental results on our palm image database denote that the proposed method is skillful for the palm segmentation and it can be used for further works.

Human Gait Recognition Based on Kernel PCA Using Projections

This paper presents a novel approach for human identification at a distance using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (PCA), to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles.

Gait Recognition Using Multiple Projections

This paper presents a new method for automatic gait recognition based on analyzing the multiple projections to silhouette using principal components analysis (PCA). Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Based on normalized correlation on the distance vectors, gait cycle estimation is first performed to extract the gait cycle. Second, an eigenspace transformation based on PCA is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles

Improved gait recognition by multiple-projections normalization

Recognizing people by gait promises to be useful for identifying individuals from a distance; in this regard, improved techniques are under development. In this paper, an improved method for gait recognition is proposed. Binarized silhouette of a motion object is first represented by four 1-D signals that are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Fourier Transform is employed as a preprocessing step to achieve translation invariant for the gait patterns accumulated from silhouette sequences that are extracted from the subjects’ walk in different speed and/or different time. Then, eigenspace transformation is applied to reduce the dimensionality of the input feature space. Support vector machine (SVM)-based pattern classification technique is then performed in the lower-dimensional eigenspace for recognition. The input feature space is alternatively constructed by using two different approaches. The four projections (1-D signals) are independently classified in the first approach. A fusion task is then applied to produce the final decision. In the second approach, the four projections are concatenated to have one vector and then pattern classification with one vector is performed in the lower-dimensional eigenspace for recognition. The experiments are carried out on the most well-known public gait databases: the CMU, the USF, SOTON, and NLPR human gait databases. To effectively understand the performance of the algorithm, the experiments are executed and presented as increasing amounts of the gait cycles of each person available during the training procedure. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.

A novel approach on silhouette based human motion analysis for gait recognition

This paper presents a novel view independent approach on silhouette based human motion analysis for gait recognition applications. Spatio-temporal 1-D signals based on the differences between the outer of binarized silhouette of a motion object and a bounding box placed around silhouette are chosen as the basic image features called the distance vectors. The distance vectors are extracted using four view directions to silhouette. Gait cycle estimation and motion analysis are then performed by using normalized correlation on the distance vectors. Initial experiments for human identification are finally presented. Experimental results on the different test image sequences demonstrate that the proposed algorithm has an encouraging performance with relatively robust, low computational cost, and recognition rate for gait-based human identification.

A new approach for human identification using gait recognition

Recognition of a person from gait is a biometric of increasing interest. This paper presents a new approach on silhouette representation to extract gait patterns for human recognition. Silhouette shape of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette. Second, eigenspace transformation based on Principal Component Analysis is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for recognition. A fusion task is finally executed to produce final decision. Experimental results on three databases show that the proposed method is an effective and efficient gait representation for human identification, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.

Developing a contactless palmprint authentication system by introducing a novel ROI extraction method

In this paper, we propose a novel contactless palmprint authentication system where the system uses a CCD camera to capture the users hand at a distance without any restrictions and touching the device. Furthermore, a novel and high performance region of interest (ROI) extraction method which makes use of nonlinear regression and palm model to extract the ROIs with high success is proposed. Comparative results indicate that the proposed ROI extraction method gives superior performance as compared to the previously proposed point-based approaches. To show the performance of the proposed system, a novel contactless database has also been created. This database includes images captured from the users who present their hands with various hand positions and orientations in cluttered backgrounds. Furthermore, experiments show that the proposed system has achieved a recognition rate of 99.488% and equal error rate of 0.277% on the contactless database of 145 people containing 1752 hand images. Display Omitted A novel unrestricted, contactless palmprint image acquisition device is developed.A novel model-based ROI extraction method, robust to the small errors is proposed.A contactless palmprint DB is constituted by collecting 1752 images from 145 subjects.The images in DB have pose, rotation, position variations and cluttered backgrounds.The results achieved by the proposed system are encouraging: 0.277% EER and 99.488% RR.