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A Data science project
About:
About:
Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media.
Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition .
Human’s faces reveal various information including gender, age and ethnicity. They provide important cues for many applications, such as biometric authentication and intelligent human-computer interface. In this paper, we present a new method that can identify humans’ genders from their face images.In the past, many researches devote to finding good image features for gender recognition.
Introduction
Introduction
Age and gender, two of the key facial attributes, play a very foundational role in social interactions, making age and gender estimation from a single face image an important task in intelligent applications, such as access control, human-computer interaction, law enforcement, marketing intelligence and visual surveillance, etc.
Human face is made up of eyes; nose, mouth and chine etc. there are differences in shape, size, and structure of these organs. So the faces are differs in thousands way. One of the common methods for face expression recognition is to extract the shape of eyes and mouth and then distinguish the faces by the distance and scale of these organs.
Age and gender Detection
Age and gender Detection
why???
why???
Abstract
Abstract
Age and gender predictions of unfiltered faces classify unconstrained real-world facial images into predefined age and gender. Significant improvements have been made in this research area due to its usefulness in intelligent real-world applications. However, the traditional methods on the unfiltered benchmarks show their incompetency to handle large degrees of variations in those unconstrained images.
More recently, Convolutional Neural Networks (CNNs) based methods have been extensively used for the classification task due to their excellent performance in facial analysis. In this work, we propose a novel end-to-end CNN approach, to achieve robust age group and gender classification of unfiltered real-world faces.
The two-level CNN architecture includes feature extraction and classification itself. The feature extraction extracts feature corresponding to age and gender, while the classification classifies the face images to the correct age group and gender. Particularly, we address the large variations in the unfiltered real-world faces with a robust image preprocessing algorithm that prepares and processes those faces before being fed into the CNN model.
Idea
Idea
Facial analysis has gained much recognition in the computer vision community in the recent past . Human’s face contains features that determine identity, age, gender, emotions, and the ethnicity of people.
Among these features, age and gender classification can be especially helpful in several real-world applications including security and video surveillance, electronic customer relationship management, biometrics, electronic vending machines, human-computer interaction, entertainment, cosmetology, and forensic art .
However, several issues in age and gender classification are still open problems. Age and gender predictions of unfiltered real-life faces are yet to meet the requirements of commercial and real-world applications in spite of the progress computer vision community keeps making with the continuous improvement of the new techniques that improve the state of the art .
Applications
Applications
1) This system can help in law enforcement field.
To identify a witness provided criminals face, how this shall be done is:
This system will help us to achieve classified list of criminals by sorting with the parameters of two things i.e. age and gender, which will sort the data or matching criminals images to more accurate image, to identify each and every detail of the suspect.
2)Online Exam Proctoring System
The CCTV can have the data of registered students while providing the seating arrangements gathered and analyze the unfair means by each and every candidate if done.
Current plan:
1) Collecting images automatically to provide face detection and recognition using opencv cv2 library.
2) Output saved in images folder ,images =100 are stored in the image folder.
3) Image classification will be done using keras to identify by using training and testing datasets ,to classify and recognise whose the image is with the help of dtasets creations.
Action Plan
Action Plan
1) First, a face detection system willnbe created by 20 may 2020.
Dataset creation,training and testing of data.
Algorithm preference is to be done.
2)Second, age and gender detection will be performed. Module is to be created by 22 May 2020 .
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