International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation based on Face Images utilizing Raspberry Pi Processor Mr. Sarang C. Zamwar, Dr. S. A. Ladhake, Mr. U. S. Ghate PG Student [Digital Electronics], Department of Electronics and Telecommunication, Sipna College of engineering and technology, Amravati (M.S.), India Principal, Sipna College of engineering and technology, Amravati (M.S.), India Assistant Professor, Department of Electronics and Telecommunication Sipna College of engineering and technology, Amravati (M.S.), India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This paper describes the method for real time
human face detection and tracking for age rank, weight and gender estimation. Face detection is involved with finding whether or no longer there are any faces in a given image and, if present, track the face and returns the face region with content of each face. Here describes a easy and convenient hardware implementation of face detection procedure utilizing Raspberry Pi, which itself is a minicomputer of a credit card size. This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age evaluation based on face images. Two main components for building an effective age estimator are facial feature extraction and estimator learning. Using feature extraction and comparing with our input data in which we have different age group face images with weight is specified according to that we also specify weight. In this article we present a novel multimodal gender estimation, which effectively integrates the head as well as mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. Facial appearance as well as head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric data from video sequences is the key approach to develop more precise and reliable realization systems. Key Words: Age rank, human face detection, Ordinal hyperplanes ranking, Raspberry pi, Tracking, Unified probabilistic framework, Weight
1. INTRODUCTION Most face detection algorithms are designed in the software domain and have a high recognition rate, but they often require several seconds to detect faces in a single image, a processing speed that is insufficient for real-time applications. A simple and easy hardware implementation of face detection system using Raspberry Pi, which itself is a minicomputer of a credit card size and is of a very low price. Automatic age estimation, which involves evaluating a person’s exact age or age-group and weight estimation, is a crucial topic in human face image understanding. A effective Š 2017, IRJET
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Impact Factor value: 5.181
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gender classification process can improve the performance of many different applications, including person recognition and smart human-computer interfaces. Here use of Raspberry Pi board as a platform for this process. Camera Pi is an excellent add-on for Raspberry Pi, to take pictures and record quality videos, with the possibility to apply a considerable range of configurations and effects. For real time and from specific image face detection, i.e. Object detection, is done and the proposed system is tested across various standard face databases, with and without noise and blurring effects. Efficiency of the system is examine by calculating the Face detection rate for each of the database. The results disclose that the proposed system can be used for face recognition even from low quality image and shows excellent performance efficiency. Automatic age estimation, which involves evaluating a person’s exact age or age-group, is a crucial topic in human face image understanding. The task of estimating exact human age adopts a dense representation of the age labels (e.g., from 0 to 80), and the task of age-group estimation divides the labels only into rough groups (e.g., elder, adult, and teenage/children). In this paper, we focus on the setting of the former task that can be applicable to more general situations. Nevertheless, the proposed method can be used for age-group estimation as well. Two main components for building an effective age estimator are facial feature extraction and estimator learning. Recognizing human gender is important since people respond differently according to gender. In addition, A effective gender classification process can improve the performance of many different applications ,including person recognition and smart human-computer interfaces. In this article, we presents the problem of automatic gender identification by exploiting the physiological and aspects of the face at the same time, we explore the possibility of using head motion, mouth motion and facial appearance in a gender identification scenario. Hence, propose a multimodal recognition approach that integrates the temporal and spatial information of the face through a probabilistic framework. ISO 9001:2008 Certified Journal
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