In recent years, attention models have been extensively used for person and vehicle re-identification. Most re-identification methods are designed to focus on key-point locations. However, depending the orientation, contribution of each varies. this paper, we present a novel dual-path adaptive model (AAVER). The global appearance path captures macroscopic features while orientation conditioned part learns capture localized discriminative by focusing most informative key-points. Through...

Heatmap regression has been used for landmark localization quite a while now. Most of the methods use very deep stack bottleneck modules heatmap classification stage, followed by to extract keypoints. In this paper, we present single dendritic CNN, termed as Pose Conditioned Dendritic Convolution Neural Network (PCD-CNN), where network is second and modular network, trained in an end fashion obtain accurate points. Following Bayesian formulation, disentangle 3D pose face image explicitly...

Keypoint detection is one of the most important pre-processing steps in tasks such as face modeling, recognition and verification. In this paper, we present an iterative method for Estimation Pose prediction unconstrained faces by Learning Efficient H-CNN Regressors (KEPLER) addressing alignment problem. Recent state art methods have shown improvements keypoint employing Convolution Neural Networks (CNNs). Although a simple feed forward neural network can learn mapping between input output...

In this paper, we present an end-to-end system for the unconstrained face verification problem based on deep convolutional neural networks (DCNN). The consists of three modules detection, alignment and is evaluated using newly released IARPA Janus Benchmark A (IJB-A) dataset its extended version Challenging set 2 (JANUS CS2) dataset. IJB-A CS2 datasets include real-world faces 500 subjects with significant pose illumination variations which are much harder than Labeled Faces in Wild (LFW)...

We propose a coarse-to-fine approach for estimating the apparent age from unconstrained face images using deep convolutional neural networks (DCNNs). The proposed method consists of three modules. first one is DCNN-based group classifier which classifies given image into groups. second module collection regressors compute fine-grained estimate corresponding in each class. Finally, any erroneous prediction corrected an error-correcting mechanism. Experimental evaluations on publicly available...

We propose an approach for age estimation from unconstrained images based on deep convolutional neural networks (DCNN). Our method consists of four steps: face detection, alignment, DCNN-based feature extraction and network regression estimation. The proposed exploits two insights: (1) Features obtained DCNN trained face-identification task can be used (2) three-layer Gaussian loss performs better than traditional methods apparent is evaluated the challenge developed ICCV 2015 ChaLearn...

In this work, we present a novel approach for vehicle speed estimation from monocular videos. The pipeline consists of modules multi-object detection, robust tracking, and estimation. tracking algorithm has the capability jointly individual vehicles estimating velocities in image domain. However, since camera parameters are often unavailable extensive variations scenes, transforming measurements domain to real world is challenging. We propose simple two-stage approximate transformation....

We present an algorithm for extracting key-point descriptors using deep convolutional neural networks (CNN). Unlike many existing CNNs, our model computes local features around a given point in image. also face alignment based on regression these descriptors. The proposed method called Local Deep Descriptor Regression (LDDR) is able to localize landmarks of varying sizes, poses and occlusions with high accuracy. Descriptors presented this paper are uniquely efficiently describe every pixel...

Digital images are the most common cover files used for steganography. In this paper, a new steganography method called JMQT based on modified quantization table is proposed. This compared with JPEG-JSteg. Two performance parameters namely capacity and stego size has been compared. As result increases increases. So provides better JPEG-JSteg stego-size.

We present new planning and learning algorithms for RAE, the Refinement Acting Engine (Ghallab, Nau, Traverso 2016). RAE uses hierarchical operational models to perform tasks in dynamically changing environments. Our procedure, UPOM, does a UCT-like search space of order find near optimal method use task context at hand. strategies acquire, from online acting experiences and/or simulated results, mapping decision contexts instances as well heuristic function guide UPOM. experimental results...

Over many decades, researchers working in object recognition have longed for an end-to-end automated system that will simply accept 2D or 3D image videos as inputs and output the labels of objects input data. Computer vision methods use representations derived based on geometric, radiometric neural considerations statistical structural matchers artificial network-based where a multi-layer network learns mapping from to class provided competing approaches problems. last four years, Deep...

Landmark detection algorithms trained on high resolution images perform poorly datasets containing low images. This degrades the performance of facial verification, recognition and modeling that rely accurate landmarks. To best our knowledge, there is no dataset consisting face along with their annotated landmarks, making supervised training infeasible. In this paper, we present a semi-supervised approach to predict landmarks by learning them from labeled The objective work show predicting...

Image degradation due to atmospheric turbulence (AT), which is common while capturing images at long ranges, adversely affects the performance of tasks such as face alignment and recognition. To best our knowledge, there does not exist any dataset consisting turbulence-degraded along with their annotated landmarks ground-truth clean images, making supervised training challenging. In this paper, we present a semisupervised method for jointly extracting facial restoring degraded by exploiting...

Landmark detection algorithms trained on high resolution images perform poorly datasets containing low images. This deters the performance of relying quality landmarks, for example, face recognition. To best our knowledge, there does not exist any dataset consisting along with their annotated making supervised training infeasible. In this paper, we present a semi-supervised approach to predict landmarks by learning them from labeled The objective work is show that predicting directly more...

A promising direction for recovering the lost information in low-resolution headshot images is utilizing a set of high-resolution exemplars from same identity. Complementary reference can improve generated quality across many different views and poses. However, it challenging to make best use multiple exemplars: alignment each exemplar cannot be guaranteed. Using low-quality mismatched as references will impair output results. To overcome these issues, we propose Headshot Image...