Neural Network-Based Face Detection
Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
computer science crazy
Super Moderator

Posts: 3,048
Joined: Dec 2008
17-09-2009, 10:24 PM

Neural Network-Based Face Detection
Abstract: We present a neural network-based upright frontal face detection system. A retinal connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting non face training examples, which must be chosen to span the entire space of non face images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented; showing that our system has comparable performance in terms of detection and false-positive rates.
Use Search at wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion
seminar addict
Super Moderator

Posts: 6,592
Joined: Jul 2011
04-02-2012, 04:34 PM

Neural Network-Based Face Detection

.pdf   rowley-ieee.pdf (Size: 522.01 KB / Downloads: 37)
In this paper, we present a neural network-based algorithm to detect upright, frontal views of faces
in gray-scale images1. The algorithm works by applying one or more neural networks directly to
portions of the input image, and arbitrating their results. Each network is trained to output the
presence or absence of a face. The algorithms and training methods are designed to be general,
with little customization for faces.
Many face detection researchers have used the idea that facial images can be characterized
directly in terms of pixel intensities. These images can be characterized by probabilistic models of
the set of face images [4, 13, 15], or implicitly by neural networks or other mechanisms [3, 12, 14,
19,21,23,25,26]. The parameters for these models are adjusted either automatically from example
images (as in our work) or by hand.

Description of the System
Our system operates in two stages: it first applies a set of neural network-based filters to an image,
and then uses an arbitrator to combine the outputs. The filters examine each location in the image at
several scales, looking for locations that might contain a face. The arbitrator then merges detections
from individual filters and eliminates overlapping detections.

2.1 Stage One: A Neural Network-Based Filter
The first component of our system is a filter that receives as input a 20x20 pixel region of the
image, and generates an output ranging from 1 to -1, signifying the presence or absence of a face,
respectively. To detect faces anywhere in the input, the filter is applied at every location in the
image. To detect faces larger than the window size, the input image is repeatedly reduced in size
(by subsampling), and the filter is applied at each size. This filter must have some invariance to
position and scale. The amount of invariance determines the number of scales and positions at
which it must be applied. For the work presented here, we apply the filter at every pixel position
in the image, and scale the image down by a factor of 1.2 for each step in the pyramid.

2.2 Stage Two: Merging Overlapping Detections and Arbitration
The examples in Fig. 3 showed that the raw output from a single network will contain a number of
false detections. In this section, we present two strategies to improve the reliability of the detector:
merging overlapping detections from a single network and arbitrating among multiple networks.

2.2.1 Merging Overlapping Detections
Note that in Fig. 3, most faces are detected at multiple nearby positions or scales, while false detections
often occur with less consistency. This observation leads to a heuristic which can eliminate
many false detections. For each location and scale, the number of detections within a specified
neighborhood of that location can be counted. If the number is above a threshold, then that location
is classified as a face. The centroid of the nearby detections defines the location of the
detection result, thereby collapsing multiple detections. In the experiments section, this heuristic
will be referred to as “thresholding”.

seminar paper
Active In SP

Posts: 6,455
Joined: Feb 2012
13-02-2012, 02:30 PM

to get information about the topic Networks -intrusion detection in multiple system full report ,ppt and related topic refer the link bellow

Important Note..!

If you are not satisfied with above reply ,..Please


So that we will collect data for you and will made reply to the request....OR try below "QUICK REPLY" box to add a reply to this page
Tagged Pages: neural network based face detection, neural network based intrusion detection system, networks intrusion detection in multiple system, neural networks based project ideas, face detection project seminar, neural network baesd face detection, neural network based face localization pdf,
Popular Searches: face detection in android report, document on braintumor detection using neural network, seminar on face detection on scribd, face detection ideas, face recognigation using nural network, neural network based clustering projects, neural network code for cancer detection,

Quick Reply
Type your reply to this message here.

Image Verification
Please enter the text contained within the image into the text box below it. This process is used to prevent automated spam bots.
Image Verification
(case insensitive)

Possibly Related Threads...
Thread Author Replies Views Last Post
Last Post: mkaasees
  FIRE AND SMOKE DETECTION USING AT89c51 seminar flower 1 723 27-12-2013, 02:22 PM
Last Post: Guest
  Fault Detection and Protection of Induction Motors Using Sensors computer science crazy 4 4,276 16-10-2013, 09:21 PM
Last Post: Guest
  Vehicle detection and compass applications using amr magnetic sensors seminar projects maker 0 376 26-09-2013, 03:57 PM
Last Post: seminar projects maker
  The pothole patrol: using a mobile sensor network for road seminar projects maker 0 516 26-09-2013, 03:56 PM
Last Post: seminar projects maker
  Magnetoresistors for vehicle detection and identification seminar projects maker 0 372 26-09-2013, 03:55 PM
Last Post: seminar projects maker
  FAULT DETECTION AND DIAGNOSIS OF 3-PHASE INVERTER SYSTEM seminar projects maker 0 439 26-09-2013, 12:32 PM
Last Post: seminar projects maker
  Virtual River Monitoring System for Bangladesh using Wireless Sensor Network Report seminar projects maker 0 475 20-09-2013, 04:45 PM
Last Post: seminar projects maker
  CONTROL OF INDUCTION MOTOR USING NEURAL NETWORKS REPORT seminar projects maker 0 311 20-09-2013, 04:34 PM
Last Post: seminar projects maker
Last Post: seminar projects maker