Final Year Project Presentation --- Magic Paint Face Supervisor Name: Korris CHUNG Co-examiner: Simon S.F. Hau Student Name: LI Deqing, Yuki Student ID: 03994486d

Content Introduction & Background System Overview System Modules Image Preprocessing Module Image Transformation Module Aging Simulation Module Aging Visualization Module Evaluations Parameter Analysis Evaluation of Aging Simulation Systems Conclusion

Introduction & Background Definition: Magic paint face is a facial aging simulation system that predicts a facial look during growth and aging process Application: Prediction of facial appearance Update of face images in identification documents Prediction on characters’ aging faces Face recognition robust to aging variation Automatic age verification

Introduction & Background (Con) Existing Researches: Statistical Face Model Wrinkle Extraction Problem to be solved: Can not study from new samples Only predict from one age scope to another Prediction of wrinkle rather than simulation Neural Network: Facial aging is an intelligent process which can be better performed by human brains rather than symbolic processors

System Overview Image Sample Database User Preprocessed Image Eigen-faces of Adult Image Aging Simulation Module Image Preprocessing Module Database Aging Visualization Module Generate Aging Image User Converged Network User image User selection Aging Image Preprocess Image Image Sample Train Neural Network System Image Transformation Module Preprocessed Image Convert Image into eigen-faces Eigen-faces of Youngster Image

Image Preprocessing Module Standard: frontal, full-face, plain expressive image with concolorous background Face Database: 55 image pairs 128*128 pixels male and female white and black Wrinkle Database 70 image pairs 64*32 pixels

Image Transformation Module Problem: Raw pixels are too huge to be fed into the neural network system Eigen-faces based on PCA Eigen-vectors derived from principal component analysis but used to represent face images Function: To transform the M pixels of a face image into N (number of images in database) eigen-face images

Image Transformation Module (Con) Each human face can be divided into N eigen-faces with different proportions Each eigen-face represents a certain feature in human face Eigen-face with higher weight means it contributes more in the original face Compression can be done by ignoring the less weighted eigen-faces

Image Transformation Module (Con)

Aging Simulation Module Neural network An artificial neuron system which simulates biological neurons Training: Map the input image onto a pre-assigned output image Testing: Feed the converged network with unseen input image Input image Input node Hidden node Output node Output image Eigen-face Coefficients Input vector Output vector

Aging Visualization Module Input: user image, selection of required section Output: aging facial image compared with input image

Parameter Analysis Eigen-faces Evaluation More eigen-faces, less error Error reduced slower as the number of eigen-faces grows larger 30% to 10% error rate for image recognition, 5% to 10% error rate for image estimation

Parameter Analysis Eigen-faces Evaluation (Con) No. of eigen-faces 5 10 15 20 Reconstructed image Not recognizable 25 30 35 40 recognizable 45 50 55 60 Almost identical 65 70 75

Parameter Analysis Hidden Node Evaluation Speed of error reduction is slower and slower Error meets minimum at 40 hidden nodes A proper number of hidden nodes = generalize A too large number of hidden nodes = memorize

Parameter Analysis Noise Evaluation Eigen-face no. Input Image 1 Output Image 1 Input Image 2 Output Image 2 original 25 30 35 40 45 Able to tolerate noise when 25 eigen-face coefficients were distorted Up from 30 eigen-face, the deviation is distinct Deviation of the output face depends on the distortion of the facial features of the input image

Parameter Analysis General Evaluation Performance of estimating female models was much poorer may be because females usually wear makeup when taking pictures female’s facial features change much when they grow up Sample ID Original Input Image Estimated by Training 1 Estimated by Training 2 Actual Output Image 1 2 3 4

Parameter Analysis Sample Evaluation Faces estimated by 5 training samples look similar to each other 20 training samples can differentiate each of the input faces Estimation of testing samples with small distance to each other may follow the same transformation pattern Sample ID Original Input Image Estimated by Training 1 Estimated by Training 2 Estimated by Training 3 Actual Output Image 1 2 3 4 5

Evaluation of Aging Simulation Systems Child to Adult Evaluation Similarity of facial features between estimated face and its desired face can be found subjectively Able to generalize different races Standard input images result in better performance Sample ID Original Input Image Estimated by Training 1 Actual Output Image 1 2 3 4 5

Evaluation of Aging Simulation Systems Adult to Child Evaluation Similarity of the facial features between estimated face and its desired face can be found subjectively Able to estimate the target age Sample ID Original Input Image Estimated by Training 1 Actual Output Image 1 2 3 4 5

Evaluation of Aging Simulation Systems Wrinkle Estimation Able to predict a natural wrinkle image Wrinkle estimation depends on the facial texture of different people Therefore, Training 2 and Training 3 are proved to be successful Original Input Image Estimated by Training 1 Estimated by Training 2 Estimated by Training 3

Evaluation of Aging Simulation Systems Linear Kernel (Support Vector Regression) Able to estimate normal aging images from unseen faces Sensitive to the quality of both the training and the testing samples Sample ID Original Input Image Estimated by Training 1 Actual Output Image 1 2 3 4 5

Evaluation of Aging Simulation Systems RBF Kernel (Support Vector Regression) Performance of estimating aging images is not satisfactory First eigen-face coefficient of each test sample is almost the same Parameters need to be fine-tune Sample ID Original Input Image Estimated by Training 1 Actual Output Image 1 2 3 4 5

Conclusion Neural network is able to predict natural aging face based on the facial features of an unseen individual. Neural network is able to generalize and predict the correct race and age. Neural network finds application in both facial aging and wrinkle aging. The performance depends on a lot of parameters. Neural network system favors standard testing samples and produces better results on them.

End Thank you for listening! Q and A