Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In trying to organize and highlight similarities and differences between the variety of multi-view learning approaches, we review a number of representative multi-view learning algorithms in different areas and classify them into three groups: 1) co-training, 2) multiple kernel learning, and 3) subspace learning. Notably, co-training style algorithms train alternately to maximize the mutual agreement on two distinct views of the data; multiple kernel learning algorithms exploit kernels that naturally correspond to different views and combine kernels either linearly or non-linearly to improve learning performance; and subspace learning algorithms aim to obtain a latent subspace shared by multiple views by assuming that the input views are generated from this latent subspace. Though there is significant variance in the approaches to integrating multiple views to improve learning performance, they mainly exploit either the consensus principle or the complementary principle to ensure the success of multi-view learning. Since accessing multiple views is the fundament of multi-view learning, with the exception of study on learning a model from multiple views, it is also valuable to study how to construct multiple views and how to evaluate these views. Overall, by exploring the consistency and complementary properties of different views, multi-view learning is rendered more effective, more promising, and has better generalization ability than single-view learning.

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A Survey on Multi-view Learning

Local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, and demographic classification. This paper presents a comprehensive survey of LBP methodology, including several more recent variations. As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial image analysis, are also highlighted.

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Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

https://www.koreascience.or.kr:443/article/JAKO200920237949770.pdf

A Survey of Face Recognition Techniques

This paper concerns the estimation of facial attributes—namely, age and gender—from images of faces acquired in challenging, in the wild conditions This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems Here, we address this problem by making the following contributions First, in answer to one of the key problems of age estimation research—absence of data—we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories We show the images in our collection to be more challenging than those offered by other face-photo benchmarks Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system These show our method to outperform state-of-the-art by a wide margin

/pdf/age-and-gender-estimation-of-unfiltered-faces-2ujy89s1qr.pdf

Age and Gender Estimation of Unfiltered Faces

In this correspondence, we address the facial expression recognition problem using kernel canonical correlation analysis (KCCA). Following the method proposed by Lyons et al. and Zhang et al. , we manually locate 34 landmark points from each facial image and then convert these geometric points into a labeled graph (LG) vector using the Gabor wavelet transformation method to represent the facial features. On the other hand, for each training facial image, the semantic ratings describing the basic expressions are combined into a six-dimensional semantic expression vector. Learning the correlation between the LG vector and the semantic expression vector is performed by KCCA. According to this correlation, we estimate the associated semantic expression vector of a given test image and then perform the expression classification according to this estimated semantic expression vector. Moreover, we also propose an improved KCCA algorithm to tackle the singularity problem of the Gram matrix. The experimental results on the Japanese female facial expression database and the Ekman's "Pictures of Facial Affect" database illustrate the effectiveness of the proposed method.

Facial expression recognition using kernel canonical correlation analysis (KCCA)

This paper presents a novel approach for gender classification by boosting local binary pattern-based classifiers. The face area is scanned with scalable small windows from which Local Binary Pattern (LBP) histograms are obtained to effectively express the local feature of a face image. The Chi square distance between corresponding Local Binary Pattern histograms of sample image and template is used to construct weak classifiers pool. Adaboost algorithm is applied to build the final strong classifiers by selecting and combining the most useful weak classifiers. In addition, two experiments are made for classifying gender based on local binary pattern. The male and female images set are collected from FERET databases. In the first experiment, the features are extracted by LBP histograms from fixed sub windows. The second experiment is tested on our boosting LBP based method. Finally, the results of two experiments show that the features extracted by LBP operator are discriminative for gender classification and our proposed approach achieves better performance of classification than several others methods.

Gender classification based on boosting local binary pattern

Locally nearest neighbor classifiers for pattern classification

Kernel principal component analysis (KPCA), introduced by Scholkopf et al., is a nonlinear generalization of the popular principal component analysis (PCA) via the kernel trick. KPCA has shown to be a very powerful approach of extracting nonlinear features for classification and regression applications. However, the standard KPCA algorithm (Scholkopf et al., 1998, Neural Computation 10, 1299--1319) may suffer from computational problem for large scale data set. To overcome these drawbacks, we propose an efficient training algorithm in this paper, and show that this approach is of much more computational efficiency compared to the previous ones for KPCA.

An Improved Algorithm for Kernel Principal Component Analysis

Speech is the most effective way for people to exchange complex information Recognition of emotional information contained in speech is one of the important challenges in the field of artificial intelligence To better acquire emotional features in speech signals, a parallelized convolutional recurrent neural network (PCRN) with spectral features is proposed for speech emotion recognition First, frame-level features are extracted from each utterance and, a long short-term memory is employed to learn these features frame by frame At the same time, the deltas and delta-deltas of the log Mel-spectrogram are calculated and reconstructed into three channels (static, delta, and delta-delta); these 3-D features are learned by a convolutional neural network (CNN) Then, the two learned high-level features are fused and batch normalized Finally, a SoftMax classifier is used to classify emotions Our PCRN model simultaneously processes two different types of features in parallel to better learn the subtle changes in emotion The experimental results on four public datasets show the superiority of our proposed method, which is better than the previous works

/pdf/parallelized-convolutional-recurrent-neural-network-with-27gjths7j9.pdf

Li Zhao

Papers

Facial expression recognition using kernel canonical correlation analysis (KCCA)

Gender classification based on boosting local binary pattern

Locally nearest neighbor classifiers for pattern classification

An Improved Algorithm for Kernel Principal Component Analysis

Parallelized Convolutional Recurrent Neural Network With Spectral Features for Speech Emotion Recognition