Facial Point Detection using Boosted Regression and Graph Models

Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point’s location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors

Deformable Part Models are Convolutional Neural Networks

Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition. They are typically viewed as distinct approaches: DPMs are graphical models (Markov random fields), while CNNs are "black-box" non-linear classifiers. In this paper, we show that a DPM can be formulated as a CNN, thus providing a novel synthesis of the two ideas. Our construction involves unrolling the DPM inference algorithm and mapping each step to an equivalent (and at times novel) CNN layer. From this perspective, it becomes natural to replace the standard image features used in DPM with a learned feature extractor. We call the resulting model DeepPyramid DPM and experimentally validate it on PASCAL VOC. DeepPyramid DPM significantly outperforms DPMs based on histograms of oriented gradients features (HOG) and slightly outperforms a comparable version of the recently introduced R-CNN detection system, while running an order of magnitude faster

Face Recognition Via GroupWise Registration Method

One of the important research area in image processing is face recognition. We introduce a new framework for tackling face recognition problem. Here propose a new way technique of face recognition problem, which is formulated as group wise deformable image registration and feature matching. The main contributions of the proposed method is to suppresses image noise without reducing the image sharpness we will use Median filtering, Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region Based on the anatomical signature calculated from each pixel, a novel Markov random field based group wise registration framework is proposed to formulate the face recognition problem. DOI: 10.17762/ijritcc2321-8169.150317

3D face tracking and multi-scale, spatio-temporal analysis of linguistically significant facial expressions and head positions in ASL

Essential grammatical information is conveyed in signed languages by clusters of events involving facial expressions and movements of the head and upper body. This poses a significant challenge for computer-based sign language recognition. Here, we present new methods for the recognition of nonmanual grammatical markers in American Sign Language (ASL) based on: (1) new 3D tracking methods for the estimation of 3D head pose and facial expressions to determine the relevant low-level features; (2) methods for higher-level analysis of component events (raised/lowered eyebrows, periodic head nods and head shakes) used in grammatical markings—with differentiation of temporal phases (onset, core, offset, where appropriate), analysis of their characteristic properties, and extraction of corresponding features; (3) a 2-level learning framework to combine lowand high-level features of differing spatio-temporal scales. This new approach achieves significantly better tracking and recognition results than our previous methods

Human Face Identification by a Markov Random Field GroupWise Registration Technique

Face recognition is widely used in various applications like in bank applications, at airport or at ATM centre for security purposes etc. There are various methods used for face recognition problem. In this paper I propose new method known as Markov field GroupWise registration in which mean of all the faces from the database will be calculated first and then this mean will be compared with the testing image. To implement these modules, four open source databases like FERET, CAS-PEAL-R1, FRGC ver. 2.0, and the LFW are required. My work will achieve good result as compared to previous methods. DOI: 10.17762/ijritcc2321-8169.15052