FACIAL RECOGNITION LOCKER FOR ANDROID

Konark Jain30th May 2014

OUTLINE• Face Detection

– What is face detection?– Importance of face detection– Current state of research– Different approaches

• One example• Face Recognition

– What is face recognition?– Its applications– Different approaches

• One example• A Video Demo

WHAT IS FACE DETECTION?

• Given an image, tell whether there is any human face, if there is, where is it(or where they are).

FACE DETECTION + RECOGNITION

• Detection accuracy affects the recognition stage• Key issues:– Correct location of key facial features(e.g. the eye

corners)– False detection– Missed detection

Project Description• FaceRecog is a FREE multi-user face recognition App that can be

used to lock your phone, block calls and pretend like a replacement for lock screen. You can disable/enable any of these features in the App settings. The App requires one "Administrator" user who can unlock the phone with a password in addition to trained faces.

• FaceRecog gives you the complete freedom by allowing you to train your face multiple times in different conditions and setting your own 'confidence threshold'. You can experiment with these features to find the optimal configuration for the App! You can also choose to have others use your phone without revealing your password by letting them enroll their faces. To configure the App for the first time, download the App to your device and follow the on-screen directions

Importance of Face Detection

• The first step for any automatic face recognition system system

• First step in many Human Computer Interaction systems– Expression Recognition– Cognitive State/Emotional State Recogntion

• First step in many surveillance systems• Tracking: Face is a highly non rigid object• A step towards Automatic Target Recognition(ATR) or

generic object detection/recognition • Video coding……

The original LBP operator, introduced by Ojala et al., is a powerful means of texture description. The operator labels the pixels of an image by thresholding the 3x3-neighbourhood of each pixel with the center value and considering the result as a binary number. Then the histogram of the labels can be used as texture descriptor, for an illustration of the basic LBP operator.Later the operator was extended to use neigbourhoods of different sizes.Using circular neighbourhoods and bilinearly interpolating the pixel values allow any radius and number of pixels in the neighbourhood. For neighbourhoods we will use the notation (P, R) which means P sampling points on a circle of radiusof R, circular (8,2) neighbourhood.Another extension to the original operator uses so called uniform patterns

ALGORITHM

USE CASE DIAGRAM

Face Detection: challenges

• Out-of-Plane Rotation: frontal, 45 degree, profile, upside down

• Presence of beard, mustache, glasses etc• Facial Expressions• Occlusions by long hair, hand• In-Plane Rotation• Image conditions:

– Size– Lighting condition– Distortion– Noise– Compression

Preprocessing

• Rotation• Scaling• Quantizing

Facial Features Detection

• Region search

FERET Database

• Training data

Face Unlock was introduced back in Ice Cream Sandwich as a fun way to unlock your phone using your face. In order to set this option up, you have to place your face inside of a face-shaped ring of dots using your front facing camera until the device decides that it knows your face enough to be able to unlock with it. Once approved, you’ll also be asked to provide a backup option in case the device cannot recognize your face. The two backup options are PIN or pattern.With Face Unlock setup, you wake your phone and then set point your front facing camera at your face. If it recognizes you, it will unlock almost immediately. If not, it will ask that you complete your backup PIN or Pattern unlock.

CONCLUSION

FUTURE WORK

• We have planned the following things that have to be done in the future:

• Increase the performance of our algorithm to give better results in less amount of time.

• Extend this project by using illumination and different postures.• Face recognition systems are usually limited in performance by lighting

conditions, facial pose and expressions. For example you might have used systems where face recognition works perfectly at home, but not in your office. Or ones where it where it confuses your face with your friend's. To overcome this, FaceRecog gives you the complete freedom by allowing you to train your face multiple times in different conditions and setting your own 'confidence threshold'