Business Assistant for Mobile Phones!Ervin Teng, Brian Jungman!

Department of Electrical Engineering, Stanford University

Experimental Results

References 1.  http//www.cs.princeton.edu/courses/

archive/fall08/cos429/CourseMaterials/Precept1/facedetect.pdf

2.  Bangpeng Yao; Haizhou Ai; Shihong Lao, "Matching texture units for face recognition," Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on , vol., no., pp.1920-1923, 12-15 Oct. 2008

3.  Han Yanbin; Yin Jianqin; Li Jinping, "Human Face Feature Extraction and Recognition Base on SIFT," Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium

4.  “Face Recognition for Mobile Phones,” Guillaume Davo; Kishore Sriadibhatla; Xing Chao, Stanford EE 368 Spring 2010

Motivation •  Assist with networking •  Prevent simple, life-changing

mistakes – something as important as important as being hired by a perspective employer can happen due to something as simple as remembering someone’s name (or not getting the job due to forgetting it)

•  Security – have security guards at the entrance to an event equipped with this app and check people when they enter

Eigenfaces

•  Look at image patch of size MxN, and resize to a MNx1 vector

•  Subtract mean of image •  Conduct transform/projection q = Wf to reduce

length from MN to J •  Store vector of size J for later comparison to

reduce comutational complexity

Future Work •  Compare performance of

Eigenfaces with the FisherFaces algorithm and implement multiple facial recognition techniques

•  Implement rotation invariance via 3-D facial recognition

•  Upon recognition of a target face, pull up the contact info of that person from the phone’s contact list

Original Image

Adding Contact Name

Capturing Training Images

Recognizing Test Image

Preprocessed Image

Displaying Business Card

Adding a New User

Detect  face  existence  using  Haar  cascade  (detectMul4Scale  OpenCV)    

Press “New Contact”

Take  and  save  5  photos  

Subtract  mean  image  and  project  eigenvectors  for  all  images  on  the  phone  

Popup  display  asking  for  the  name  of  the  new  user  

Conduct  image  preprocessing  (convert  to  grayscale,  crop  and  resize  photo,  and  perform  

histogram  equaliza4on)  

Take  business  card  photo  

Recognizing a User

Detect  face  using  Haar  cascade  (detectMul4Scale  OpenCV)    

Press “Recognize Face”

Subtract  mean  image  and  project      eigenvector  for  test  image  

Determine  which  training  image’s  eigenvector  is  closest  to  the  test  image’s  eigenvector  

Lookup  and  display  name  of  user  in  test  image  

Conduct  image  preprocessing  (convert  to  grayscale,  crop  and  resize  photo,  and  perform  

histogram  equaliza4on)  

If  buMon  is  clicked,  display  business  card