Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep 3D face reconstruction approach that 1) leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and 2) performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation. Our method is fast, accurate, and robust to occlusion and large pose. We provide comprehensive experiments on three datasets, systematically comparing our method with fifteen recent methods and demonstrating its state-of-the-art performance.
Face image quality is an important factor affecting the accuracy of automatic face recognition. It is usually possible for practical recognition systems to capture multiple face images from each subject. Selecting face images with high quality for recognition is a promising stratagem for improving the system performance. We propose a learning to rank based framework for assessing the face image quality. The proposed method is simple and can adapt to different recognition methods. Experimental result demonstrates its effectiveness in improving the robustness of face detection and recognition.
As a novel digital video steganography, the motion vector (MV)-based steganographic algorithm leverages the MVs as the information carriers to hide the secret messages. The existing steganalyzers based on the statistical characteristics of the spatial/frequency coefficients of the video frames cannot attack the MV-based steganography. In order to detect the presence of information hidden in the MVs of video streams, we design a novel MV recovery algorithm and propose the calibration distance histogram-based statistical features for steganalysis. The support vector machine (SVM) is trained with the proposed features and used as the steganalyzer. Experimental results demonstrate that the proposed steganalyzer can effectively detect the presence of hidden messages and outperform others by the significant improvements in detection accuracy even with low embedding rates.
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