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The Partial Least Squares (PLS) algorithm has been widely applied in face recognition in recent years. However, all the improved algorithms of PLS did not utilize non-negativity and sparsity synchronously to improve the recognition accuracy and robustness. In order to solve these problems, this paper proposes a novel algorithm named Two-Dimension Non-negative Sparse Partial Least Squares (2DNSPLS),...
Illumination variation that span globally and locally across the facial surface is one of the most important aspect in designing a robust face recognition system. The illumination variations due to changes in lighting conditions could produce different shape of shading on the face thus deforming the facial features. The effect of these variations is simply more severe in the presence of single-sample...
Average neighborhood margin maximization (ANMM) is a feature extraction method to make homogeneous points collect as near as possible and heterogeneous points disperse as far away as possible. To enhance the anti-noise ability of ANMM, correntropy based average neighborhood margin maximization (CANMM) is proposed in this paper. This method utilizes correntropy to substitute the Euclidean distance...
Automatic face detection is the essential part of the facial expression recognition (FER) systems. Before investigating the facial expressions, it is compulsory to detect and extract the faces first from the expression frames. Existing methods often involve modeling of the face detection that normally necessitates huge amount of training data and cannot efficiently tackle changes over time. In this...
With advances in sensor technology, three dimensional (3D) face has become an emerging biometric modality, preferred especially in high security applications. However, dealing with facial occlusions is a great challenge, which should be handled to enable applicability to fully automatic security systems. In this paper, we propose a technique to deal with occlusions at the classification stage: A masking...
Object tracking is a difficult task in computer vision, which is usually affected by color, surrounding illumination, variation of the object's appearance and other factors. In previous years, many algorithms can only set up fixed appearance models to track object. Recently, more and more tracking algorithms have been proposed to deal with object appearance variation and illumination change. However,...
A human-computer interaction system for an automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. In this paper, an Automatic Facial Expression Recognition System (AFERS) has been proposed. The proposed method has three stages: (a) face detection, (b) feature...
The Scalp EEG is a large-scale & robust information source about neocortical dynamic functions. In this paper, we analyze a scalp Electro Encephalogram (EEG) database of 33 human subjects during the cognitive activity of Meditation, specifically Kriya Yoga. The information measures such as Renyi, Shannon entropies and Relative Energy of the different EEG Bands such as Alpha, Beta, & delta...
In this paper we present a novel approach to face recognition. We propose an adaptation and extension to the state-of-the-art methods in face recognition, such as sparse representation-based classification and its extensions. Effectively, our method combines the sparsity-based approaches with additional least-squares steps and exhitbits robustness to outliers achieving significant performance improvement...
Security robot has become one of the most important research topics over the past decades. A number of robots have been designed to safeguard human life and wealth. This paper focuses on design and implementation of mobile robot with three subsystems: The obstacle avoidance, face recognition and detection leakage of combustible gases. In the first subsystem, an implementation of artificial neural...
Neighborhood Preserving Embedding (NPE) is a famous graph-oriented dimension deduction algorithm, which has got lots of successful applications in computer vision field. Just as all the graph-oriented algorithms, the effectiveness of the NPE greatly relies on whether a suitable graph can be constructed. While the traditional graph construction method has some intrinsic weaknesses, especially when...
We present a new approach to localize extensive facial landmarks with a coarse-to-fine convolutional network cascade. Deep convolutional neural networks (DCNN) have been successfully utilized in facial landmark localization for two-fold advantages: 1) geometric constraints among facial points are implicitly utilized, 2) huge amount of training data can be leveraged. However, in the task of extensive...
Facial Recognition is probably one of the most commonly used biometrie characteristics used by humans for recognition. This is one of the reasons why it has been subject of intense research for the past 30 years or so. In this time a lot of work is being done not only in the development of stable, real time facial recognition system but also in acquiring different modalities of facial imagery for...
We introduce a novel algorithm namely Robust Normalized Cross-correlation Coefficient (RNCC) for 2D frontal face recognition with expression variation. There are thirteen renowned ways to look at the Cross-correlation Coefficient. Our proposed method makes use of the technique named "Correlation as a Rescaled Variance of the Difference between Standardized Scores". It is based on estimating...
Detection of positive and negative emotions can provide an insight into the person's level of satisfaction, social responsiveness and clues like the need for help. Therefore, automatic perception of affect valence is a key for novel human-computer interaction applications. However, robust recognition with conventional 2D cameras is still not possible in realistic conditions, in the presence of high...
We propose an online robust object tracking algorithm based on a sample-based dictionary. The sample-based dictionary in our method means that the over-completely dictionary of sparse coding algorithm is formed by using the sample basis extracted from video images. Different from the other tracking methods that use the object features and a set of boosted classifiers, the proposed algorithm considers...
Regularized linear regression based representation techniques for face recognition (FR) have attracted a lot of attention in past years. The l_1-regularized sparse representation based classification (SRC) method achieves state-of-the-art results in FR. However, recently several studies have shown the role of collaborative representation (CR) that plays a crucial role for the success of SRC in robust...
Most dimensionality reduction methods are usually based on dissimilarity measurement of pixel intensities which can not obtain a more robust dissimilarity measurement. To address this problem, in this paper, we propose a novel robust dimensionality reduction method from Laplacian orientations. This method does not directly manipulate pixel intensity, which introduces Laplacian orientations, combined...
Person re-identification is becoming a hot research topic due to its academic importance and attractive applications in visual surveillance. This paper focuses on solving the relatively harder and more importance multiple-shot re-identification problem. Following the idea of treating it as a set-based classification problem, we propose a new model called Locality-constrained Collaborative Sparse Approximation...
In this paper, we propose a novel facial expression recognition method under partial occlusion based on Gabor multi-orientation features fusion and local Gabor binary pattern histogram sequence (LGBPHS). Firstly, the Gabor filter is adopted to extract multi-scale and multi-orientation features. Secondly, the Gabor magnitudes of different orientations in the same scale will be fused according to the...
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