Faces play a magnificent role in human robot interaction, as they do in our daily life. The inherent ability of the human mind facilitates us to recognize a person by exploiting various challenges such as bad illumination, occlusions, pose variation etc. which are involved in face recognition. But it is a very complex task in nature to identify a human face by humanoid robots. The recent literatures on face biometric recognition are extremely rich in its application on structured environment for solving human identification problem. But the application of face biometric on mobile robotics is limited for its inability to produce accurate identification in uneven circumstances. The existing face recognition problem has been tackled with our proposed component based fragmented face recognition framework. The proposed framework uses only a subset of the full face such as eyes, nose and mouth to recognize a person. It's less searching cost, encouraging accuracy and ability to handle various challenges of face recognition offers its applicability on humanoid robots. The second problem in face recognition is the face spoofing, in which a face recognition system is not able to distinguish between a person and an imposter (photo/video of the genuine user). The problem will become more detrimental when robots are used as an authenticator. A depth analysis method has been investigated in our research work to test the liveness of imposters to discriminate them from the legitimate users. The implication of the previous earned techniques has been used with respect to criminal identification with NAO robot. An eyewitness can interact with NAO through a user interface. NAO asks several questions about the suspect, such as age, height, her/his facial shape and size etc., and then making a guess about her/his face.
The usability and practicality of any machine learning (ML) applications are largely influenced by two critical but hard-to-attain factors: low latency and low cost. Unfortunately, achieving low latency and low cost is very challenging when ML depends on real-world data that are highly distributed and rapidly growing (e.g., data collected by mobile phones and video cameras all over the world). Such real-world data pose many challenges in communication and computation. For example, when training data are distributed across data centers that span multiple continents, communication among data centers can easily overwhelm the limited wide-area network bandwidth, leading to prohibitively high latency and high cost. In this dissertation, we demonstrate that the latency and cost of ML on highly-distributed and rapidly-growing data can be improved by one to two orders of magnitude by designing ML systems that exploit the characteristics of ML algorithms, ML model structures, and ML training/serving data. We support this thesis statement with three contributions. First, we design a system that provides both low-latency and low-cost ML serving (inferencing) over large-scale and continuously-growing datasets, such as videos. Second, we build a system that makes ML training over geo-distributed datasets as fast as training within a single data center. Third, we present a first detailed study and a system-level solution on a fundamental and largely overlooked problem: ML training over non-IID (i.e., not independent and identically distributed) data partitions (e.g., facial images collected by cameras varies according to the demographics of each camera's location).
ABSTRACT Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification have evolved enormously over the past few decades. Sketch recognitio n is one of the most important areas that have evolved as an integral component adopted by the agencies of law administration in curren t trends of forensic science. Matching of derived sketches to photo images of face is also a difficult assignment as the considered sketches are produced upon the verbal explanation depicted by the eye witness of the crime scene and may have scarcity of se nsitive elements that exist in the photograph as one can accurately depict due to the natural human error. Substantial amount of the novel research work carried out in this area up late used recognition system through traditional extraction and classificat ion models . But very recently, few researches work focused on using deep learning techniques to take an advantage of learning models for the feature extraction and classification to rule out potential domain challenges. The first part of this review paper basically focuses on deep learning techniques used in face recognition and matching which as improved the accuracy of face recognition technique with training of huge sets of data. This paper also includes a survey on different techniques used to match com posite sketches to human images which includes component - based representation approach, automatic composite sketch recognition technique etc. INTRODUCTION As per the researches carried out, a complete face recognition system includes two patterns of face detection and face recognition: 1) Structural similarity and 2) individual local differences of human faces. Therefore, it is required to extract the features of the face through the face detection process. The evolution of face recognition is due to its technical challenges and huge potential application in video surveillance, identity authorization, multimedia applications, home and office security, law enforcement and different human - computer interaction activities. Facial recognition technology (FRT) is one of the most controversial new tools. It was first devel oped in the 1960s.
Applying machine learning techniques to security solutions is one of the current AI trends. This article will cover the approach to developing OCR-based software using deep learning algorithms. This software can be used to analyze and process identification such as a US driver's license as part of a security system for verifying identity. OCR (Optical Character Recognition) technology is already used by machine learning companies for business processes automation and optimization, with use cases ranging from Dropbox using it to parse through pictures to Google Street view identifying different street signs to searching through text messages and translating text in real time. In this particular case, OCR can be used as part of an automated biometric verification system.
In the past few years, there's been a dramatic rise in the adoption of face recognition, detection, and analysis technology. You're probably most familiar with recognition systems, like Facebook's photo-tagging recommender and Apple's FaceID, which can identify specific individuals. Detection systems, on the other hand, determine whether a face is present at all; and analysis systems try to identify aspects like gender and race. All of these systems are now being used for a variety of purposes, from hiring and retail to security and surveillance. Many people believe that such systems are both highly accurate and impartial.