Wende Zhang

Biometrics-based cryptographic key generation

Instead of using PINs and passwords as cryptographic keys that are either easy to forget or vulnerable to dictionary attacks, easy-to-carry and difficult-to-transfer keys can be generated based on user-specific biometric information. A framework is proposed to generate stable cryptographic keys from biometric data that is unstable in nature. The proposed framework differs from prior work in that user-dependent transforms are utilized to generate more compact and distinguishable features. Thereby, a longer and more stable bitstream can be generated as the cryptographic key. Experiments are performed on a face database to verify the feasibility of the proposed framework. The preliminary result is very encouraging.

Optimal thresholding for key generation based on biometrics

In this paper, we introduce a novel method for key generation based on biometrics. Given the biometrics, a set of reliable features are extracted. Each feature is compared with multiple thresholds to generate a multi-bit key. By cascading the multi-bit keys, we obtain one biokey that can be used for security applications. In order to generate a reliable bio-key, an optimal thresholding method is proposed to minimize the authentication error rate, in terms of the false accept rate (FAR) and the false reject rate (FRR). The experimental results show that the proposed approach of key generation is user-friendly and reliable.

Principle component analysis and its variants for biometrics

Principle component analysis (PCA) has been widely used for analyzing the statistics of data. While applied to biometrics as a classification scheme, PCA faces certain challenges. We present a number of modifications to PCA in order to meet these challenges. Using face recognition as an example, we show how eigenflow, PCA applied to optimal flow, enables us to measure the difference between two images while allowing expression changes and registration error. We show how PCA can be updated to model time-varying statistics. We also show that PCA can be used to model the surface reflectance of human faces and reduce illumination variation that defeats most existing face recognition algorithms. Finally, we present distinguishing component analysis (DCA) and apply it to multimodal biometric authentication.

Prior-based vanishing point estimation through global perspective structure matching

In this paper, we describe a prior-based vanishing point estimation method through global perspective structure matching (GPSM). In contrast to the traditional approaches which require an undistorted image with straight roads for vanishing point estimation, our method first infers vanishing point candidates of an input image from an image database with pre-labeled vanishing points. An image-based retrieval method is used to identify the best candidate images in the database by matching images perspective structure. The initial estimation of input images vanishing point is calculated from the pre-labeled vanishing points of the best candidates. Probabilistic refinement (PR) is then used to optimize the vanishing point estimate. Experimental results show that the proposed method works well in a variety of on-road driving environments (e.g., in urban, highway and country-side areas), especially with traffic captured by a fish-eye back-aid camera.

Example-based clear path detection assisted by vanishing point estimation

With the goal of avoiding obstacles on the road using only a single camera during autonomous driving, we propose an example-based clear path detection method that also considers the additional perspective cue from the estimated vanishing point. First, because of the benefit that the vanishing point provides knowledge for clear path detection, we apply an example-based method to estimate initial vanishing point candidates. Then, instead of building a pre-trained clear path model over the limited training set, we propose an example-based global image matching method to get an approximate idea of clear path candidate regions, and use a Gaussian Mixture Model (GMM) for local image patch modeling to further improve the clear path detection. Finally, we develop an iterative probabilistic refinement to improve performance of both components. Experimental results of real road scenes are presented to illustrate the effectiveness of the proposed method.

Personal authentication based on generalized symmetric max minimal distance in subspace

We introduce an improved classification algorithm based on the concept of symmetric maximized minimal distance in subspace (SMMS). Given the training data of authentic samples and imposter samples in the feature space, our previous approach, SMMS, tried to identify a subspace in which all the authentic samples were projected onto the origin and all the imposter samples were far away from the origin. The optimality of the subspace was determined by maximizing the minimal distance between the origin and the imposter samples in the subspace. The generalized SMMS relaxes the constraint of fitting all the authentic samples to the origin in the subspace to achieve the optimality and considers the optimal direction of the linear support-vector machines (SVM) as a feasible solution in our optimization procedure to guarantee that our result is no worse than the linear SVM. We present a procedure to achieve such optimality and to identify the subspace and the decision boundary. Once the subspace is trained, the verification procedure is simple since we only need to project the test sample onto the subspace and compare it against the decision boundary. Using face authentication as an example, we show that the proposed algorithm outperforms the linear classifier based on SMMS and SVM. The proposed algorithm also applies to multimodal feature spaces. The features can come from any modalities, such as face images, voices, fingerprints, etc.

Camera-based clear path detection

In using image analysis to assist a driver to avoid obstacles on the road, traditional approaches rely on various detectors designed to detect different types of objects. We propose a framework that is different from traditional approaches in that it focuses on finding a clear path ahead. We assume that the video camera is calibrated offline (with known intrinsic and extrinsic parameters) and vehicle information (vehicle speed and yaw angle) is known. We first generate perspective patches for feature extraction in the image. Then, after extracting and selecting features of each patch, we estimate an initial probability that the patch corresponds to clear path using a support vector machine (SVM) based probability estimator on the selected features. We finally perform probabilistic patch smoothing based on spatial and temporal constraints to improve the initial estimate, thereby enhancing detection performance. We show that the proposed framework performs well even in some challenging examples with shadows and illumination changes.

Generalized optimal thresholding for biometric key generation using face images

In this paper, we study biometric key generation using face images. Given a face image, a set of biometric features are extracted. Each feature is compared with a threshold to generate a key-bit. By cascading the key-bits from all the features, we obtain one bio-key that can be used for security applications. The performance of a biometric key generation system, determined by the chosen thresholds, can be evaluated according to reliability and security. A generalized optimal thresholding method is proposed in this paper to improve the reliability by minimizing the authentication error rate and the security by maximizing the entropy of the generated key.

Strong shadow removal via patch-based shadow edge detection

Detecting objects in shadows is a challenging task in computer vision. For example, in clear path detection application, strong shadows on the road confound the detection of the boundary between clear path and obstacles, making clear path detection algorithms less robust. Shadow removal, relies on the classification of edges as shadow edges or non-shadow edges. We present an algorithm to detect strong shadow edges, which enables us to remove shadows. By analyzing the patch-based characteristics of shadow edges and non-shadow edges (e.g., object edges), the proposed detector can discriminate strong shadow edges from other edges in images by learning the distinguishing characteristics. In addition, spatial smoothing is used to further improve shadow edge detection. Numerical experiments show convincing results that shadows on the road are either removed or attenuated with few visual artifacts, which benefits the clear path detection. In addition, we show that the proposed method outperforms the state-of-art algorithms in different conditions.

Security analysis for key generation systems using face images

In this paper, we analyze the security problem of user information associated key generation (UIAKG) systems. We consider three kinds of attacks from the hacker: the exhaustive search attack, the authentic key statistics attack and the device key statistics attack. Under each attack, we give the estimate of the number of guesses the hacker has to make in order to access the system. Such analysis provides useful guidelines in designing a UIAKG system. The analysis also suggests that a user-dependent UIAKG is more secure than a user-independent one. Two UIAKG systems using face images as inputs are designed and compared to support the above theoretical analysis.