Tracking Human Faces in Real-Time,

1995 ◽  
Author(s):  
Jie Yang ◽  
Alex Waibel
Keyword(s):  
Real Time ◽  
Human Faces

scholarly journals Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6104
Author(s):  
Bernardo Calabrese ◽  
Ramiro Velázquez ◽  
Carolina Del-Valle-Soto ◽  
Roberto de Fazio ◽  
Nicola Ivan Giannoccaro ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Visually Impaired ◽  
High Speed ◽  
Low Cost ◽  
Recognition Rate ◽  
Object Identification ◽  
Time Interval ◽  
Solar Powered ◽  
Human Faces

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

Real-time detection of human faces in uncontrolled environments

1997 ◽  
Author(s):  
Ara V. Nefian ◽  
Mehdi Khosravi ◽  
Monson H. Hayes III
Keyword(s):  
Real Time ◽  
Real Time Detection ◽  
Human Faces

3D Face Modeling for Multi-Feature Extraction for Intelligent Systems

2013 ◽  
pp. 1145-1161
Author(s):  
Zahid Riaz ◽  
Suat Gedikli ◽  
Michael Beetz ◽  
Bernd Radig
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Real Time ◽  
Intelligent Systems ◽  
Industrial Robot ◽  
Image Synthesis ◽  
Face Model ◽  
3D Face ◽  
3D Face Modeling ◽  
Human Faces

In this chapter, we focus on the human robot joint interaction application where robots can extract the useful multiple features from human faces. The idea follows daily life scenarios where humans rely mostly on face to face interaction and interpret gender, identity, facial behavior and age of the other persons at a very first glance. We term this problem as face-at-a-glance problem. The proposed solution to this problem is the development of a 3D photorealistic face model in real time for human facial analysis. We also discuss briefly some outstanding challenges like head poses, facial expressions and illuminations for image synthesis. Due to the diversity of the application domain and optimization of relevant information extraction for computer vision applications, we propose to solve this problem using an interdisciplinary 3D face model. The model is built using computer vision and computer graphics tools with image processing techniques. In order to trade off between accuracy and efficiency, we choose wireframe model which provides automatic face generation in real time. The goal of this chapter is to provide a standalone and comprehensive framework to extract useful multi-feature from a 3D model. Such features due to their wide range of information and less computational power, finds their applications in several advanced camera mounted technical systems. Although this chapter focuses on multi-feature extraction approach for human faces in interactive applications with intelligent systems, however the scope of this chapter is equally useful for researchers and industrial practitioner working in the modeling of 3D deformable objects. The chapter mainly specified to human faces but can also be applied to other applications like medical imaging, industrial robot manipulation and action recognition.

scholarly journals Implementation of Aadhaar Verified, face Recognition based Security Surveillance

2020 ◽  
Vol 9 (9) ◽  
pp. 415-420
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Human Face ◽  
Element Determination ◽  
Python Language ◽  
Cascade Calculation ◽  
Haar Cascade ◽  
Two Phases ◽  
Security Surveillance ◽  
Human Faces

Security and Authentication is a basic piece of any industry. In Real time, Human face acknowledgment can be acted in two phases, for example, Face discovery and Face acknowledgment. This paper actualizes "Haar-Cascade calculation" to distinguish human faces which are sorted out in Open CV by Python language. Gathering with other existing calculations, this classifier creates a high acknowledgment rate even with shifting articulations, effective element determination and low combination of bogus positive highlights. Haar highlight based course classifier framework uses just 200 highlights out of 6000 highlights to yield an acknowledgment pace of 85-95%.

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