Age from Faces in the Deep Learning Revolution

Project Code :TCMAPY304

Objective

Facial images are increasingly used for age estimation research purposes. In this application, we are developing a system to predict the age of a person using their video feed. It employs the use of computer vision and deep learning techniques.

Abstract

In this study, we introduce an approach to classify gender and age from images of human faces which is an essential part of our method for autonomous detection of anomalous human behaviour. Human behaviour is often uncertain, and sometimes it is affected by emotion or environment. Automatic detection can help to recognise human behaviour which later can assist in investigating suspicious events. Central to our proposed approach is the recently introduced transfer learning. It was used on the basis of deep learning and successfully applied to image classification area. This paper is a continuous study from previous research on heterogeneous data in which we use images as supporting evidence. We present a method for image classification based on a pretrained deep model for feature extraction and representation followed by a CNN classifier. Because very few data sets with labels of gender and age exist of face images, we build one dataset named GA Face and applied our proposed method to this dataset achieving excellent results and robustness.

KEYWORDS: Computer Vision, CNN, Classification, Unfiltered images, Gender recognition, Age recognition.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE SPECIFICATIONS:

  • Processor: I3/Intel
  • Processor RAM: 4GB (min)
  • Hard Disk: 128 GB
  • Key Board: Standard Windows Keyboard
  • Mouse: Two or Three Button Mouse
  • Monitor: Any

SOFTWARE SPECIFICATIONS:

  • Operating System: Windows 7+
  • Server-side Script: Python 3.6+
  • IDE: PyCharm
  • Libraries Used: Pandas, Numpy, Matplotlib,OS,OpenCV.

Learning Outcomes

  • Importance of Supervised & Unsupervised Learning.
  • Scope of age determination system.
  • Use of Deep learning.
  • What are pre trained models.
  • Implementation of computer vision.
  • What is OpenCV.
  • How face detection works.
  • Importance of PyCharm IDE.
  • Benefits of pre trained models.
  • Process of debugging a code.
  • Input and Output modules.
  • How test the project based on user inputs and observe the output.
  • Project Development Skills:
    • Problem analyzing skills.
    • Problem solving skills.
    • Creativity and imaginary skills.
    • Programming skills.
    • Deployment.
    • Testing skills.
    • Debugging skills.
    • Project presentation skills.

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