Over the past two decades, a number of face recognition methods have been proposed in the literature. Most of them use holistic face images to recognize people. However, human faces are easily occluded by other objects in many real-world scenarios and we have to recognize the person of interest from his/her partial faces. In this paper, we propose a new partial face recognition approach by using feature set matching, which is able to align partial face patches to holistic gallery faces automatically and is robust to occlusions and illumination changes. Given each gallery image and probe face patch, we first detect key points and extract their local features. Then, we propose a Metric Learned Extended Robust Point Matching (MLERPM) method to discriminatively match local feature sets of a pair of gallery and probe samples. Lastly, the similarity of two faces is converted as the distance between two feature sets. Experimental results on three public face databases are presented to show the effectiveness of the proposed approach.

For many practical face recognition systems such as law enforcement, e-passport, and ID card identification, there is usually only a single sample per person (SSPP) enrolled in these systems, and many existing face recognition methods may fail to work well because there are not enough samples for discriminative feature extraction in this scenario. However, the probe samples of these face recognition systems are usually captured on the spot, and it is possible to collect multiple face images per person for on-location probing, which is potentially useful to improve the recognition performance. In this paper, we propose a method based on locality repulsion projections (LRP) and a sparse reconstruction-based similarity measure (SRSM) to address the problem of SSPP face recognition using multiple probe images. The LRP method is motivated by our observation that similar face images from different people may lie in a locality in the feature space and cause misclassifications. We design the method with the aim of separating the samples of different classes within a neighborhood through subspace projections for easier classification. To better characterize the similarity between each gallery face and the probe image set, we propose a SRSM method for assigning a label to each probe image set. Experimental results on five widely used face datasets are presented to demonstrate the effectiveness of the proposed approach.

Recent research in visual data classification often involves image sets and the measurement of dissimilarity between each pair of them. An effective solution is to model each image set using a subspace and compute the distance between these two subspaces as the dissimilarity between the sets. Several subspace similarity measures have been proposed in the literature. However, their relationships have not been well explored and most of them do not fully utilize the different importance of individual bases of each subspace. To consolidate this family of subspace-based measures, we propose a generalized subspace distance (GSD) framework and show that most existing subspace similarity measures can be considered as its special cases. To better utilize the different importance, we further propose a new fractional order weighted subspace distance (FOWSD) method within the GSD framework, by assigning different weights to the bases of each subspace and thus characterizing their different importance in similarity measurement. Experimental results on two image classification tasks including face recognition and object recognition are presented to show the effectiveness of the proposed method.

It is not uncommon to see several videos of almost identical content on the internet. These near duplicates, coupled with the sheer number of videos, pose a big challenge to the effective organization of video clips online. We propose an adaptive classification approach to detect near-duplicate versions, and an integrated voting strategy to group clusters and to elect a representative for each cluster. Our proposed methods are based on our observation that near-duplicate videos usually span a small, albeit variable area in the feature space, while videos of different contents are scattered far apart. The classification method aims to select a suitable threshold by maximizing the margin for each video sequence in the similarity space, and the voting scheme focuses on merging subsets with mutual information based on neighbor information and inverted indices. Experimental results on an unconstrained web dataset including over 10000 videos demonstrate the efficacy of the proposed methods.

We propose a no-reference method for estimating the peak signal-to-noise ratio (PSNR) of images subject to quantization noise in the block-based discrete cosine transform (bDCT) domain. The proposed method uses bDCT coefficients with shifted block boundary positions from the compressed image to estimate the Laplace distribution parameter of the bDCT coefficients of the original image. The resulting distributions are used to estimate the quantization noise power and the PSNR of the compressed image.

This paper presents a new method for estimating the relative compression distortion among images encoded from the same original. Increasingly, many compressed versions of the same image are available over the Internet, but users may not have access to the original to measure their objective quality. We propose to cross-check each image against the quantization constraints of another version and obtain a conservative estimation of the difference in mean squared error (ΔMSE), indicating the relative objective quality between these images. Our proposed method is able to judge the relative quality correctly among compressed images with PSNR difference of as little as 0.5 dB, and it is applicable to images in mixed compression formats, such as JPEG and JPEG2000.

A single source video may be compressed using different encoders with different settings. In the context of online video sharing, many such video copies exist, and an end user may have access to a few of them and would like to reconstruct a video sequence with quality superior to all available copies. In this paper, we propose a scheme to improve the video quality by projecting the reconstructed video onto the quantization constraint sets defined by multiple video copies. Experimental results show that the proposed method is capable of improving video quality both subjectively and objectively.

A single source image can be compressed into many different copies with different quantization parameters. An end user may have access to a few of such copies (e.g., over the Internet), but would like to obtain one that has superior quality to any available copy. We propose in this paper to improve the reconstruction quality by using regularization and refining the narrowed quantization constraint set (NQCS) from such image copies. Since the size of NQCS is generally large, we also discuss the estimation of initial images for regularization by applying proper probability models. Experimental results show that the proposed scheme can consistently improve the quality of the reconstructed images in both subjective and objective terms.