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Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. Generally, deep neural network architectures are stacks consisting of a large number of convolutional layers, and they perform downsampling along the spatial dimension via pooling to reduce memory usage. Concurrently, the feature map dimension (i.e., the number of channels) is...
In this paper, we propose a discriminative keypoint selection-based 3D face recognition method that is superior to prevalent techniques in terms of both computational complexity and performance. We use the average face model (AFM) for face registration to efficiently locate the axis of symmetry in the rotated face mesh and recover a full frontal face from a 3D face model commonly corrupted due to...
We propose a three-dimensional (3D) face-modelling method from a single two-dimensional (2D) face image using a gallery of 2D face images and their corresponding 3D face models. Unlike existing methods, which require human effort, we provide a simple way to reconstruct 3D face models without user interaction. Our main approach is based on the idea that a particular coefficient that linearly combines...
In this paper, we introduce a novel approach to automatically detect salient regions in an image. Our approach consists of global and local features, which complement each other to compute a saliency map. The first key idea of our work is to create a saliency map of an image by using a linear combination of colors in a high-dimensional color space. This is based on an observation that salient regions...
Facial age estimation is a process of identifying the age of a single face in an image or a video. Since age information can be used in many environments such as security, surveillance, and entertainment, age estimation has recently received much attention from researchers. In this paper, we propose an automatic age estimation method via extended curvature Gabor (ECG) features and a learning-based...
In this paper, we propose a novel unsupervised feature selection method: Simultaneous Orthogonal basis Clustering Feature Selection (SOCFS). To perform feature selection on unlabeled data effectively, a regularized regression-based formulation with a new type of target matrix is designed. The target matrix captures latent cluster centers of the projected data points by performing orthogonal basis...
In this paper, we introduce a novel technique to automatically detect salient regions of an image via high-dimensional color transform. Our main idea is to represent a saliency map of an image as a linear combination of high-dimensional color space where salient regions and backgrounds can be distinctively separated. This is based on an observation that salient regions often have distinctive colors...
The training time of Adaboost to obtain the strong classifier is usually time-consuming. Moreover, to deal with rotated faces, it is natural to need much more processing time for both training and execution stages. In this paper, we propose new efficient and fast multi-view face detection method based on Adaboost. From the robustness property of Harr-like feature, we first construct the strong classifier...
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