Identification of Facial Features on Android Platforms Josette. C. Tagatio Mawafo1, W.A. Clarke1 and P.E. Robinson1 University of Johannesburg, Johannesburg, South Africa Published in: Industrial Technology (ICIT), 2013 IEEE International Conference

Abstract present and investigate the performance of an algorithm designed to identify facial features on an android mobile platform

Feature Point Extraction A. Skin Seqmentation The RGB color image is transformed into the YCrCb color image using the following formula:

Feature Point Extraction A. Skin Seqmentation

Feature Point Extraction(cont.) B. Eye Extraction How to extract the face？ Skin region =1.618 Including face , neck and part of chest width of the head Height of the head

Feature Point Extraction(cont.) B. Eye Extraction

Feature Point Extraction(cont.) B. Eye Extraction

Feature Point Extraction(cont.) To page 16 Feature Point Extraction(cont.) B. Eye Extraction

Feature Point Extraction(cont.) B. Eye Extraction mark Left iris Right iris

Feature Point Extraction(cont.) C. Prediction Feature Regions (1) N / D = 0.33 D：The distance between the two eyes D N： vertical distance between the eyes and the nose tip N

Feature Point Extraction(cont.) C. Prediction Feature Regions (2) M / D = 1.10 D：The distance between the two eyes D M： the vertical distance between the eyes and the mouth center M

Feature Point Extraction(cont.) C. Prediction Feature Regions (3) E = 0.8 D D：The distance between the two eyes D E：The width of the nose E

Feature Point Extraction(cont.) C. Prediction Feature Regions (4) E / L = Phi Phi = 1.618 E = 0.8D L = the distance from the nose to the mouth E L

Feature Point Extraction(cont.) C. Prediction Feature Regions (5) K / E = Phi Phi = 1.618 E：0.8 D K：the length of the lips E K

Feature Point Extraction(cont.) C. Prediction Feature Regions (6) The width of the eyes is similar to the nose width width of the eyes E = 0.8D

Feature Point Extraction(cont.) To：Page 7 Feature Point Extraction(cont.) D. Localizing Features Points On the binary image obtained from applying the threshold to the non- maximum suppressed image we notice that we have an edge detected image where only the most important parts of the face are highlighted.

Experimental Result A. Database And Testing Methology (1) 500 facial images of different individuals from the UJ(University Of Johannesburg) database. (2) The size of each image is 480 * 624. (3) The images are named 0000x.jpg, where x ranges for 000 to 500.

Experimental Result(cont.) A. Database And Testing Methology (4) images are taken under different conditions: facial expression open-eyes; smiling / non-smiling and facial details glasses / no glasses. (5) Eyes need to be open (6) images are grey homogeneous background and are in upright, frontal.

Experimental Result(cont.) B. Prediction Accuracy Of The Nose And Mouth

Experimental Result(cont.) C. Performance Of The Feature Detector Android device：Samsung galaxy 10.1 tablet processing capabilities： (1 GHz dual core NVIDIA Tegra 2 processor, and 1GB RAM ) The java implementation was run on 45 images, the average processing time is recorded in table II