Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-of-the-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for face recognition task. Specifically, the center loss simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers. More importantly, we prove that the proposed center loss function is trainable and easy to optimize in the CNNs. With the joint supervision of softmax loss and center loss, we can train a robust CNNs to obtain the deep features with the two key learning objectives, inter-class dispension and intra-class compactness as much as possible, which are very essential to face recognition. It is encouraging to see that our CNNs (with such joint supervision) achieve the state-of-the-art accuracy on several important face recognition benchmarks, Labeled Faces in the Wild (LFW), YouTube Faces (YTF), and MegaFace Challenge. Especially, our new approach achieves the best results on MegaFace (the largest public domain face benchmark) under the protocol of small training set (contains under 500000 images and under 20000 persons), significantly improving the previous results and setting new state-of-the-art for both face recognition and face verification tasks.

/pdf/a-discriminative-feature-learning-approach-for-deep-face-2zrw7nm451.pdf

A Discriminative Feature Learning Approach for Deep Face Recognition

The PASCAL Visual Object Classes Challenge

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge 1 show the superiority of A-Softmax loss in FR tasks.

/pdf/sphereface-deep-hypersphere-embedding-for-face-recognition-bof1bk9snh.pdf

SphereFace: Deep Hypersphere Embedding for Face Recognition

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

In this paper, we present a novel probabilistic approach to detecting and tracking human faces in video sequences. Specifically, with the use of multimodal observations a graphical chain model integrating smooth regularization is proposed. Using this model, the recovery of faces that are not detected or detected with only low confidence by a frame-based detector can be formulated as an overall optimization problem constrained by a set of reference faces, which are initially detected with high certainties. Experimental results demonstrate the effectiveness of our approach.

A unified probabilistic approach to face detection and tracking

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.

Video organization: Near-Duplicate Video clustering

Conventional face recognition usually assumes that both the training and test phases employ the same form of data in a face recognition system. In many real world face recognition applications such as e-passport and ID card identification, it is common to have only a single sample per person enrolled or recorded in these systems because it is generally difficult to collect additional samples for training. In the testing phase, however, the probe samples are usually captured on spot and it is possible to collect a number of face samples for recognition. This problem is defined as image-to-set face recognition in this paper, which is essentially different from most existing image-to-image or set-to-set face recognition problems. Specifically, we propose in this paper a new locality repulsion projections (LRP) method to address this problem. Motivated by the fact that interclass face samples with higher similarity usually lie in a locality and are more easily misclassified than those with lower similarity, we aim to learn a mapping to project the original face samples onto a low-dimensional feature subspace such that samples lying in a locality are repulsed and more discriminative information can be exploited for recognition. To better characterize the similarity between a gallery sample and a testing set, we further propose a reconstruction-based point-to-set similarity measure to identify the unlabeled subjects. Experimental results on two widely used face databases are presented to demonstrate the efficacy of the proposed method.

Locality repulsion projections for image-to-set face recognition

This paper presents a novel study and analysis on two important problems in gait recognition: one is how to perform gait recognition with only segment of a complete gait cycle for test and the other is how static and dynamic information affect the recognition results in such situation. In conventional gait recognition research, the gait sequence of at least one cycle is usually needed to ensure the completeness of gait dynamics. And in this paper we will show that this is not a necessary condition and considerable recognition results could still be achieved with only part of the cycle based on appropriate similarity measure algorithms. Moreover, we will also show the different roles that static and dynamic information play in such situation through experimental study. To our knowledge, there is little work on these important problems, results from which can make gait recognition more applicable in frequently happened scenarios such as occlusion.

A novel study and analysis on segmental gait sequence recognition

While deployment of wireless channels has become widespread and fast-growing for mobile applications, transmitting data over these existing error-prone networks can be very unreliable and challenging due to time-varying interference and channel errors. Many error-resilient algorithms have been proposed to provide adequate resilient features in order to protect video data from channel errors. However, these algorithms often aim to achieve the optimal decoded video quality in terms of mean square error without any consideration for the visual quality. In this paper, we present a perceptually error-resilient method for video transcoding based on the attention-based intra refresh technique and the characteristics of the human visual system to enhance the perceptual performance of the transcoded video. Specifically, the foveated just noticeable distortion and visual attention models are employed to estimate the perceptual loss impact due to error propagation for allocating intra-refreshed macroblocks in the transcoded video. Experimental results show that the proposed method can achieve a much better performance than the existing methods in terms of both the visual quality and perceptual quality measure.

Yap-Peng Tan

Papers

A unified probabilistic approach to face detection and tracking

Video organization: Near-Duplicate Video clustering

Locality repulsion projections for image-to-set face recognition

A novel study and analysis on segmental gait sequence recognition

Perceptually optimized error resilient transcoding using attention-based intra refresh