Distance metric optimization driven convolutional neural network for age invariant face recognition

TL;DR: Major deep learning concepts pertinent to face image analysis and face recognition are reviewed, and a concise overview of studies on specific face recognition problems is provided, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching.

TL;DR: This work reconstructs the high-dimensional features of Android applications (apps) and employ multiple CNN to detect Android malware and proposes a hybrid model based on deep autoencoder (DAE) and convolutional neural network (CNN), which shows powerful ability in feature extraction and malware detection.

TL;DR: A novel model to enhance the recognition accuracy of the short utterance speaker recognition system is proposed using a convolutional neural network to process spectrograms, which can describe speakers better and gains the considerable accuracy as well as the reasonable convergence speed.

TL;DR: The prime objective of this research is to sum-up recent face recognition techniques and develop a broad understanding of how these techniques behave on different datasets and present future aspects of face recognition technologies and its potential significance in the upcoming digital society.

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.

TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.

TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.

TL;DR: A face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression is developed and the proposed “Fisherface” method has error rates that are significantly lower than those of the Eigenface technique when tested on the same database.