192 research outputs found
Facial Age Estimation
Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the huge amount of human photos in the social networks. These images may provide no age label, but it is easily to derive the age difference for an image pair of the same person. To improve the age estimation accuracy, we propose a novel learning scheme to take advantage of these weakly labeled data via the deep Convolutional Neural Networks (CNNs). For each image pair, Kullback-Leibler divergence is employed to embed the age difference information(MS. SWATHI THILAKAN). The entropy loss and the cross entropy loss are adaptively applied on each image to make the distribution exhibit a single peak value. The combination of these losses is designed to drive the neural network to understand the age gradually from only the age difference information. Experimental results on two aging face databases show the advantages of the proposed age difference learning system and the state-of-the-art performance is gained
Spatial-Spectral Manifold Embedding of Hyperspectral Data
In recent years, hyperspectral imaging, also known as imaging spectroscopy,
has been paid an increasing interest in geoscience and remote sensing
community. Hyperspectral imagery is characterized by very rich spectral
information, which enables us to recognize the materials of interest lying on
the surface of the Earth more easier. We have to admit, however, that high
spectral dimension inevitably brings some drawbacks, such as expensive data
storage and transmission, information redundancy, etc. Therefore, to reduce the
spectral dimensionality effectively and learn more discriminative spectral
low-dimensional embedding, in this paper we propose a novel hyperspectral
embedding approach by simultaneously considering spatial and spectral
information, called spatial-spectral manifold embedding (SSME). Beyond the
pixel-wise spectral embedding approaches, SSME models the spatial and spectral
information jointly in a patch-based fashion. SSME not only learns the spectral
embedding by using the adjacency matrix obtained by similarity measurement
between spectral signatures, but also models the spatial neighbours of a target
pixel in hyperspectral scene by sharing the same weights (or edges) in the
process of learning embedding. Classification is explored as a potential
strategy to quantitatively evaluate the performance of learned embedding
representations. Classification is explored as a potential application for
quantitatively evaluating the performance of these hyperspectral embedding
algorithms. Extensive experiments conducted on the widely-used hyperspectral
datasets demonstrate the superiority and effectiveness of the proposed SSME as
compared to several state-of-the-art embedding methods
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing
problem that presents a unique challenge for face recognition systems and even
for humans. The problem regarding occlusion is less covered by research when
compared to other challenges such as pose variation, different expressions,
etc. Nevertheless, occluded face recognition is imperative to exploit the full
potential of face recognition for real-world applications. In this paper, we
restrict the scope to occluded face recognition. First, we explore what the
occlusion problem is and what inherent difficulties can arise. As a part of
this review, we introduce face detection under occlusion, a preliminary step in
face recognition. Second, we present how existing face recognition methods cope
with the occlusion problem and classify them into three categories, which are
1) occlusion robust feature extraction approaches, 2) occlusion aware face
recognition approaches, and 3) occlusion recovery based face recognition
approaches. Furthermore, we analyze the motivations, innovations, pros and
cons, and the performance of representative approaches for comparison. Finally,
future challenges and method trends of occluded face recognition are thoroughly
discussed
Image Restoration
This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with
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