Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, few benchmark data sets assembled facilitate study. However, there sizable gap between actual application needs current state art. In paper we make following contributions. (a) We present comprehensive database labeled...

This paper concerns the estimation of facial attributes-namely, age and gender-from images faces acquired in challenging, wild conditions. problem has received far less attention than related face recognition, particular, not enjoyed same dramatic improvement capabilities demonstrated by contemporary recognition systems. Here, we address this making following contributions. First, answer to one key problems research-absence data-we offer a unique data set images, labeled for gender,...

"Frontalization" is the process of synthesizing frontal facing views faces appearing in single unconstrained photos. Recent reports have suggested that this may substantially boost performance face recognition systems. This, by transforming challenging problem recognizing viewed from viewpoints to easier constrained, forward poses. Previous frontalization methods did attempting approximate 3D facial shapes for each query image. We observe shape estimation photos be a harder than and can...

Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face performance sky-rocketed using deep-learning classic datasets like LFW, leading to belief that this technique reached human performance, it still remains an open problem unconstrained environments as demonstrated newly released IJB datasets. This survey aims summarize main advances and, more general,...

The 3D shapes of faces are well known to be discriminative. Yet despite this, they rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom serious but often overlooked problem with existing methods single view reconstruction: when applied in the wild, their estimates either unstable change different photos same subject or over-regularized generic. In response, we describe robust method regressing discriminative morphable models (3DMM)....

Although surveillance video cameras are now widely used, their effectiveness is questionable. Here, we focus on the challenging task of monitoring crowded events for outbreaks violence. Such scenes require a human surveyor to monitor multiple screens, presenting crowds people in constantly changing sea activity, and identify signs breaking violence early enough alert help. With this mind, propose following contributions: (1) We describe novel approach real-time detection scenes. Our method...

We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, FSGAN is subject agnostic can be applied to pairs of faces without requiring training on those faces. To this end, we describe a number technical contributions. derive novel recurrent neural network (RNN)-based approach reenactment which adjusts both pose expression variations single image or video sequence. For sequences, introduce continuous interpolation the views based reenactment, Delaunay...

Computer vision systems have demonstrated considerable improvement in recognizing and verifying faces digital images. Still, appearing unconstrained, natural conditions remains a challenging task. In this paper, we present face-image, pair-matching approach primarily developed tested on the "Labeled Faces Wild" (LFW) benchmark that reflects challenges of face recognition from unconstrained The propose makes following contributions. 1) We family novel face-image descriptors designed to...

We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) face recognition problems. Unlike settings often assumed there, far less labeled data is typically available training emotion classification systems. therefore designed with goal simplifying problem domain by removing confounding factors input images, an emphasis image illumination variations. This, in effort to reduce amount...

We show that even when face images are unconstrained and arbitrarily paired, swapping between them is quite simple. To this end, we make the following contributions. (a) Instead of tailoring systems for segmentation, as others previously proposed, a standard fully convolutional network (FCN) can achieve remarkably fast accurate segmentations, provided it trained on rich enough example set. For purpose, describe novel data collection generation routines which provide challenging segmented...

Existing single view, 3D face reconstruction methods can produce beautifully detailed results, but typically only for near frontal, unobstructed viewpoints. We describe a system designed to provide reconstructions of faces viewed under extreme conditions, out plane rotations, and occlusions. Motivated by the concept bump mapping, we propose layered approach which decouples estimation global shape from its mid-level details (e.g., wrinkles). estimate coarse acts as foundation then separately...

Man-made scenes are often densely packed, containing numerous objects, identical, positioned in close proximity. We show that precise object detection such remains a challenging frontier even for state-of-the-art detectors. propose novel, deep-learning based method detection, designed settings. Our contributions include: (1) A layer estimating the Jaccard index as quality score; (2) novel EM merging unit, which uses our scores to resolve overlap ambiguities; finally, (3) an extensive,...

We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates parameters (weights) of decoder. Furthermore, to allow maximal adaptivity, weights at each decoder block vary spatially. For this purpose, we design new type hypernetwork, composed nested U-Net for drawing higher level context features, multi-headed weight generating module immediately before they are consumed, efficient memory utilization, primary that is novel dynamic patch-wise...

We propose a method for detecting face swapping and other identity manipulations in single images. Face methods, such as DeepFake, manipulate the region, aiming to adjust appearance of its context, while leaving context unchanged. show that this modus operandi produces discrepancies between two regions (e.g., Fig. 1). These offer exploitable telltale signs manipulation. Our approach involves networks: (i) identification network considers region bounded by tight semantic segmentation, (ii)...

We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation without detection or landmark localization. observe that estimating the 6DoF rigid transformation a is simpler problem than facial detection, often used for alignment. In addition, offers more information bounding box labels. leverage these observations to make multiple contributions: (a) describe an easily trained, efficient, Faster R-CNN–based model which regresses all faces in photo, preliminary detection. (b)...

The One-Shot Similarity (OSS) kernel [3, 4] has recently been introduced as a means of boosting the performance face recognition systems. Given two vectors, their score (Fig. 1) reflects likelihood each vector belonging to same class other and not in defined by fixed set “negative” examples. In this paper we explore how may nevertheless benefit from availability such labels. (a) present system utilizing identity pose information improve facial image pair-matching using multiple scores; (b)...

This paper concerns the problem of facial landmark detection. We provide a unique new analysis features produced at intermediate layers convolutional neural network (CNN) trained to regress coordinates. shows that while being processed by CNN, face images can be partitioned in an unsupervised manner into subsets containing faces similar poses (i.e., 3D views) and properties (e.g., presence or absence eye-wear). Based on this finding, we describe novel CNN architecture, specialized...

We present a data-driven method for estimating the 3D shapes of faces viewed in single, unconstrained photos (aka "in-the-wild"). Our was designed with an emphasis on robustness and efficiency - explicit goal deployment real-world applications which reconstruct display 3D. key observation is that many practical applications, warping shape reference face to match appearance query, enough produce realistic impressions query's shape. Doing so, however, requires matching visual features between...

We present a novel means of describing local image appearances using binary strings. Binary descriptors have drawn increasing interest in recent years due to their speed and low memory footprint. A known shortcoming these representations is inferior performance compared larger, histogram based such as the SIFT. Our goal close this gap while maintaining benefits attributed representations. To end we propose Learned Arrangements Three Patch Codes descriptors, or LATCH. key observation that...

We introduce our method and system for face recognition using multiple pose-aware deep learning models. In representation, a image is processed by several pose-specific convolutional neural network (CNN) models to generate features. 3D rendering used poses from the input image. Sensitivity of pose variations reduced since we use an ensemble CNN The paper presents extensive experimental results on effect landmark detection, layer selection model performance pipeline. Our novel representation...