Automatic Face Naming by Learning Discriminative Affinity Matrices from Weakly Labeled Images

Automatic Face Naming by Learning   Discriminative Affinity Matrices from Weakly Labeled Images

Abstract

Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularize to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach.

Architecture Diagram

 

 

 

Existing System

In this project is used to detect the face of movie characters and recognize the characters and the existing system are taking the too much time to detect the face. But this one we can do it in a minute process.

Disadvantages:

·       In the previous process the time taken for detecting face is too long in windows processed.

Proposed System

          Automatic Face Naming by Learning Discriminative Affinity Matrices from Weakly Labeled Images is used to detect the face of movie characters and the Proposed system is taking the minimum time to detect the face. In this one we can do it in a minute process.

Advantages:

·       In the proposed process the time taken for detecting face in minimum (min) time only in windows processed.

Modules

1.    Design & Explain with Login

2.    Detection

3.     Recognition

1.    Login

In this module is going to explain the Automatic Face Naming by Learning Discriminative Affinity Matrices From Weakly Labeled Images designing and how we did the face detection and recognition in this project. The images will explain about the facial fetching details. After that admin going to login with the details which needed for the login page.

2.     Detection

In this module we are going to detect the face of the movie characters. In this module we are using the emgu cv library we must install the emgu cv library. After installing the emgu cv lib in our project we need to add reference with the name emgu.cv, emgu.cv.util, emgu.cv.ui. When you will complete the references you will get the emgu controls in the toolbox.

3.     Recognition

In this module we are going to recognize the face of the movie characters which is we previously stored on the face database. We just found that the give the real name of it. This is going to be done here. Here we are using the With the help of these Eigen Object Recognizer we are going to recognize the face.

HARDWARE & SOFTWARE REQUIREMENTS:

 HARDWARE REQUIREMENTS: 

·                     System                  :         Pentium IV 2.4 GHz.

·                     Hard Disk              :         40 GB.

·                     Floppy Drive         :         1.44 Mb.

·                     Monitor                 :         15 VGA Color.

·                     Mouse                   :         Logitech.

·                     Ram                      :         512 MB.

  SOFTWARE REQUIREMENTS: 

·                     Operating system   :         Windows XP Professional.

·                     Coding Language   :         C#.NET