Human face recognition based on multidimensional PCA and extreme learning machine

In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.

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A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering

Curvelet based face recognition via dimension reduction

Infrared face recognition: A comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.

Infrared face recognition: a comprehensive review of methodologies and databases

This paper proposes a new method for face recognition based on a multiresolution analysis tool called Digital Curvelet Transform. Multiresolution ideas notably the wavelet transform have been profusely employed for addressing the problem of face recognition. However, theoretical studies indicate, digital curvelet transform to be an even better method than wavelets. In this paper, the feature extraction has been done by taking the curvelet transforms of each of the original image and its quantized 4 bit and 2 bit representations. The curvelet coefficients thus obtained act as the feature set for classification. These three sets of coefficients from the three different versions of images are then used to train three Support Vector Machines. During testing, the results of the three SVMs are fused to determine the final classification. The experiments were carried out on three well known databases, viz., the Georgia Tech Face Database, AT&T "The Database of Faces" and the Essex Grimace Face Database.

Face recognition by curvelet based feature extraction

The Relationship Between Grain Size Distribution and Ductile to Brittle Transition Temperature in FeCrAl Alloys

Owing to their excellent mechanical properties, carbon fiber-reinforced polymer (CFRP) composites have a broad spectrum of applications in aerospace, civil engineering, automotive, and numerous industrial fields. Despite their many advantages, the inherent lack of thermal stability of the polymer matrix results in the loss of the composite’s mechanical properties when exposed to elevated temperatures. In an effort to provide thermal protection, a multilayer film composed of tris(hydroxymethyl)-aminomethane (THAM)-buffered polyethylenimine (PEI) and vermiculite (VMT) clay was deposited on CFRP composites via layer-by-layer assembly. When subjected to the flame from a butane torch and mechanical loading, the polymer–clay nanobrick wall provides substantial thermal insulation, decreasing the temperature on the backside of the CFRP composites by as much as 100 °C. The PEI-THAM/VMT coating also helps to maintain the storage modulus of the composite and offers significant protection from oxidative degradation, as confirmed by dynamic mechanical analysis and X-ray photoelectron spectroscopy. The performance of this polymer–clay multilayer film provides excellent thermal barrier that can be used to protect advanced composite materials from extreme heat. 

Tanaya Mandal

Papers

Curvelet based face recognition via dimension reduction

Face recognition by curvelet based feature extraction

The Relationship Between Grain Size Distribution and Ductile to Brittle Transition Temperature in FeCrAl Alloys

Heat-Shielding Nanobrick Wall for Carbon Fiber-Reinforced Polymer Composites