Ping-Han Lee

Illumination Compensation Using Oriented Local Histogram Equalization and its Application to Face Recognition

Illumination compensation and normalization play a crucial role in face recognition. The existing algorithms either compensated low-frequency illumination, or captured high-frequency edges. However, the orientations of edges were not well exploited. In this paper, we propose the orientated local histogram equalization (OLHE) in brief, which compensates illumination while encoding rich information on the edge orientations. We claim that edge orientation is useful for face recognition. Three OLHE feature combination schemes were proposed for face recognition: 1) encoded most edge orientations; 2) more compact with good edge-preserving capability; and 3) performed exceptionally well when extreme lighting conditions occurred. The proposed algorithm yielded state-of-the-art performance on AR, CMU PIE, and extended Yale B using standard protocols. We further evaluated the average performance of the proposed algorithm when the images lighted differently were observed, and the proposed algorithm yielded the promising results.

Automatic Gender Recognition Using Fusion of Facial Strips

We propose a fully automatic system that detects and normalizes faces in images and recognizes their genders. To boost the recognition accuracy, we correct the in-plane and out-of-plane rotations of faces, and align faces based on estimated eye positions. To perform gender recognition, a face is first decomposed into several horizontal and vertical strips. Then, a regression function for each strip gives an estimation of the likelihood the strip sample belongs to a specific gender. The likelihoods from all strips are concatenated to form a new feature, based on which a gender classifier gives the final decision. The proposed approach achieved an accuracy of 88.1% in recognizing genders of faces in images collected from the World-Wide Web. For faces in the FERET dataset, our system achieved an accuracy of 98.8%, outperforming all the six state-of-the-art algorithms compared in this paper

Face verification and identification using Facial Trait Code

We propose the Facial Trait Code (FTC) to encode human facial images. The proposed FTC is motivated by the discovery of some basic patterns existing in certain local facial features. We call these basic patterns Distinctive Trait Patterns (DTP), which can be extracted from a large number of faces. We have also found that the fusion of these DTPs can accurately capture the appearance of a face. The extraction of DTP involves clustering and boosting for maximizing the discrimination between human faces. The extracted DTPs can be symbolized and used to make up the n-ary facial trait codes. A given face can be encoded at some prescribed facial traits to render an n-ary facial trait code with each symbol in its codeword corresponding to the closest DTP. We applied FTC to a face identification and verification problems with 3575 facial images from 840 people under different illumination conditions, and it yielded satisfactory results.

Subject-Specific and Pose-Oriented Facial Features for Face Recognition Across Poses

Most face recognition scenarios assume that frontal faces or mug shots are available for enrollment to the database, faces of other poses are collected in the probe set. Given a face from the probe set, one needs to determine whether a match in the database exists. This is under the assumption that in forensic applications, most suspects have their mug shots available in the database, and face recognition aims at recognizing the suspects when their faces of various poses are captured by a surveillance camera. This paper considers a different scenario: given a face with multiple poses available, which may or may not include a mug shot, develop a method to recognize the face with poses different from those captured. That is, given two disjoint sets of poses of a face, one for enrollment and the other for recognition, this paper reports a method best for handling such cases. The proposed method includes feature extraction and classification. For feature extraction, we first cluster the poses of each subjects face in the enrollment set into a few pose classes and then decompose the appearance of the face in each pose class using Embedded Hidden Markov Model, which allows us to define a set of subject-specific and pose-priented (SSPO) facial components for each subject. For classification, an Adaboost weighting scheme is used to fuse the component classifiers with SSPO component features. The proposed method is proven to outperform other approaches, including a component-based classifier with local facial features cropped manually, in an extensive performance evaluation study.

Cascading Multimodal Verification using Face, Voice and Iris Information

In this paper we propose a novel fusion strategy which fuses information from multiple physical traits via a cascading verification process. In the proposed system users are verified by each individual modules sequentially in turns of face, voice and iris, and would be accepted once he/she is verified by one of the modules without performing the rest of the verifications. Through adjusting thresholds for each module, the proposed approach exhibits different behavior with respect to security and user convenience. We provide a criterion to select thresholds for different requirements and we also design an user interface which helps users find the personalized thresholds intuitively. The proposed approach is verified with experiments on our in-house face-voice-iris database. The experimental results indicate that besides the flexibility between security and convenience, the proposed system also achieves better accuracy than its most accurate module.

Real-time pedestrian and vehicle detection in video using 3D cues

Existing pedestrian and vehicle detection algorithms use 2D cues of objects, such as pixel values, color, texture, shape information or motion. The use of 3D cues in object detection, on the other hand, is not well studied in the literature. In this paper, we propose an efficient algorithm that detects pedestrian and vehicle using their 3D cues. The proposed algorithm first detects moving objects in a video frame using a background modeling technique. For each moving object, we extract its width and height in 3D space, with the aid of the intrinsic and extrinsic parameters of the camera monitoring the scene. To estimate the camera parameters, we apply a calibration-free method, which simply requires users to specify six vertices on a cuboid in the scene. Then based on its 3D cues, a object is verified whether it is a pedestrian(vehicle) or not by the class-specific Support Vector Machine (SVM). In our experiment, the proposed algorithm achieves a precision of 88.2%(89.1%) for pedestrian(vehicle) detection, at 32 frame-per-second on average upon five testing sequences.

Facial Trait Code

We propose a facial trait code (FTC) to encode human facial images, and apply it to face recognition. Extracted from an exhaustive set of local patches cropped from a large stack of faces, the facial traits and the associated trait patterns can accurately capture the appearance of a given face. The extraction has two phases. The first phase is composed of clustering and boosting upon a training set of faces with neutral expression, even illumination, and frontal pose. The second phase focuses on the extraction of the facial trait patterns from the set of faces with variations in expression, illumination, and poses. To apply the FTC to face recognition, two types of codewords, hard and probabilistic, with different metrics for characterizing the facial trait patterns are proposed. The hard codeword offers a concise representation of a face, while the probabilistic codeword enables matching with better accuracy. Our experiments compare the proposed FTC to other algorithms on several public datasets, all showing promising results.

Facial Trait Code and Its Application to Face Recognition

We propose the Facial Trait Code (FTC) to encode human facial images. The proposed FTC is motivated by the discovery of the basic types of local facial features, called facial trait bases , which can be extracted from a large number of faces. In addition, the fusion of these facial trait bases can accurately capture the appearance of a face. Extraction of the facial trait bases involves clustering and boosting approaches, leading to the best discrimination of the human faces. The extracted facial trait bases are symbolized and make up the n-ary facial trait codes. A given face can be then encoded at the patches specified by the traits to render an n-ary facial trait code with each symbol in its codeword corresponding to the closest trait base. We applied FTC to a typical face identification problem, and it yielded satisfactory results under different illumination conditions.

Distinctive Personal Traits for Face Recognition Under Occlusion

Existing local feature methods for face recognition utilize visually salient regions around eye, nose, and mouth to model the characteristics of a person. The premise of such an approach is that there exists a set of features that are common in all human faces and yet distinct to tell one from the rest apart. In this paper we present an algorithm that selects the best set of features or templates for each individual, and uses these distinct personal traits to boost face recognition performance even when they are partially occluded. Borne out by numerous experiments and comparisons, we demonstrate that the proposed method is effective in recognizing faces with partial occlusion and variation in expression.

Robust face recognition using probabilistic facial trait code

Recently, Facial Trait Code (FTC) was proposed for solving face recognition, and was reported with promising recognition rates. However, several simplifications in the FTC encoding make it unable to handle the most rigorous face recognition scenario in which only one facial image per individual is available for enrollment in the gallery set and the probe set includes faces under variations caused by illumination, expression, pose or misalignment. In this study, we propose the Probabilistic Facial Trait Code (PFTC) with a novel encoding scheme and a probabilistic codeword distance measure. We also proposed the Pattern-Specific Subspace Learning (PSSL) scheme that encodes and recognizes faces robustly under aforementioned variations. The proposed PFTC was evaluated and compared with state-of-the-art algorithms, including the FTC, the algorithm using sparse representation, and the one using Local Binary Pattern. Our experimental study considered factors such as the number of enrollment allowed in the gallery, the variation among gallery or probe set, and reported results for both identification and verification problems. The proposed PFTC yielded significant better recognition rates in most of the scenarios than all the states-of-the-art algorithms evaluated in this study.