Object_Detection_For_Faces

Build a Face Detection model

Objective of this notebook

• The purpose of this notebook is to build a Face Detection model
• Details of the problem statement , data set , summary of the code/solution , sample output/Prediction from the program and final result of the project are listed in the sections to follow.

Problem Statement

Computer Vision can be used to detect faces which is useful in multiple domains.In this particular use case , a company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene.

Data Description:

The dataset comprises of images and its mask where there is a human face

Domain:

Entertainment

Summary of the Solution/Code:

The code aims at building a face detection model with an additional requirement of displaying the the face object with a mask.

• We begin by doing an Exploratory Data analyses and Visualisation/viewing of the images
• We then do the required pre-processing for the data to make it compatible with the model to be built.
• We have been given only the bounding box as labels in training data ,masked images for/as labels have not been given.Since we have been specifically asked to create face masks , we will be using the bounding box it self as the mask and treat this problem as a sematic segmentation problem.
• We will use an algorithm that does semantic segmentation to model the same .We will be using Mask-RCNN.
• We build the model using a reliable third part implementation of Mask-RCNN(Mask R-CNN Project developed by Matterport) which has been built on top of the Keras deep learning framework
• Essentialy , what we do in the code is load a pre-trained Mask-RCNN model , omit the top layers .
• We then re-train the model with "faces" data & tune to get the best results/minimum loss
• Finally we evaluate our model on Test data with the chosen metric as mAp.
• Refer python worksheet Project_P1_FaceMaskDetection.ipynb for the solution

Sample Ouput/Prediction :

Here is a sample result/output from the program/model

Result

• We have obtained a .80 MAP on Train Set & a 0.681 on Test Set
• Further training of model would have given us better results
• This can be considered as an extesion to improec the performace of this model