Face demographics for age estimation using multi-task deep neural networks.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Structure of today’s talk:

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

• Brief history of face recognition.• Various nets and their uses.• Image feature hierarchy.• The difficulty of age estimation.• Weaknesses of current age architectures.• Potential solutions to weaknesses• The proposed architectures.• Questions.

Brief history of automated facial recognition.• Semi automated facial recognition system W. Bledsoe et al 1966A, 1966B, for shadowy

organization, required assistance with picking face features.

• First linear algebra detection algorithms come into existence in the late 1980s using PCA analysis.

• Followed by multi-linear PCA analysis, Linear discriminant analysis and Independent component analysis

• In the early to mid 1990s methods based on elastic graph algorithms began to be used reaching accuracies well above 96% when tested against images in a control environment.

• During this same period work based on support vector machines was published. These used labelled training data, and achieved higher accuracies than elastic graph algorithms.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Brief history of automated facial recognition.• 1986 advent of back propagation algorithm designed by David Rumelhart on 2 layer

networks.

• First recorded use of Convolution Neural Networks for temporal signal processing, and digit classification was the Neo-cognitron in the 1988, LeNet-5 first hand written digit classifier in 2003.

• Earliest paper I could find on CNN’s for face recognition is from 1997 published by the IEEE Computational Society.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Various neural network topologies and their uses.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Various neural network topologies and their uses.

Siamese network.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Recurrent neural network.

Cascade network.

Various neural network topologies.

Convolution Neural network

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Neocognitronconceptual network.

Deep ArchitecturesAlexNet.

Basic Multi-task classification architecture date, WACVI 2014.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Difference between Multi-task vs Single task classification loss functions.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Standard loss function minimized by gradient descent.

Difference between Multi-task vs Single task classification loss functions.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

VS.

The problem of face age detection.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Depends on a myriad of inter related subtle factors:• Ethnicity.• Gender.• Life style.• Position of the eyes, nose and mouth.• Texture of skin

Best age classifiers.

JFLDNN: 2014

Hyperface: 2016, DLA architectureSimilar, 2015

DEX, 2015

Best age classifiers: weaknesses.

JFLDNN: 2014 Hyperface: 2016

DEX, 2015

Though promising when it comes to Gender, it fails at age classification. However it highlights the potential use of concatenating intermediate Layers.

This points out the problem of over regularization and extended training time of AlexNet.

DEX didn’t take advantage of face landmarks.

The problem of regularization.

Multi-task task wise dropout classification architecture.Dropout for auxiliary tasks:Problem: Each task has different loss functions.Possible solution: Covariance matrix learning is not an option, as tasks have the same loss function.Task-wise early stopping: This stops overfitting but different from weight regularization

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

The problem of regularization.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

The problem of limited training data

The problem of limited training data

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Sequential training independent of data set.

The problem of long training times.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

+

Squeeze Net, 2016

Proposed Architectures, A.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Proposed Architectures, B.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Proposed data sets, FGNET and MORPH Data sets.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Morph:labels: D.O.BGender labels: Yes

Race: yes

FG Net:labels: D.O.BGender labels: YesIn the wild: No

Proposed: Evaluation metrics.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Proposed: Hardware and Software.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Proposed: Hardware and Software.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Future proposed project Goals• Implement architecture in Matlab and Matconvnet.

• Test against DLA and DEX architectures using FGNET and MORPH data sets.

• Interface with single page web app, to make interactive website.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Further work.• Future work will see these same architectures used to characterize

other features such as ethnicity and emotion.

• The networks main image hierarchy can be switched to a newer leaner and/or deeper convolution network.

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

References:1. Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou, Facial Landmark detection by deep multi-task learning, Tang Dept. of

Information Engineering, The Chinese University of Hong Kong, 2014.

2. Gil Levi and Tal Hassner, Age and Gender Classification using Convolutional Neural Networks, Department of Mathematics and Computer Science, The Open University of Israel, 2015

3. Rajeev Ranjan, Vishal M. Patel and Rama Chellappa, HyperFace: A deep multi-task learning framework for face detection, landmark localization, pose estimation an gender recognition, 2016

4. Yuxin Jiang, Songbin Li, Peng Liu and Qiongxing Dai, Multi-feature deep learning for face gender recognition, 2014, IEEE 7th

Joint International Information Technology and Artificial Intelligence Conference, 2014

5. R. Rothe, Timofter R. and L. Gool, DEX: Deep expectation of apparent age from a single image, Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 10-15, 2015

6. Zafeiriou Stefanos, Cha Zhang and Zhengyou Zhang, A survey on face detection in the wild: past, present and future. Computer Vision and image understanding. Computer Vision and Image understanding, 4(138):1-24, 2015

7. Forrest N Iandola, Matthew W Moskewicz, Khalid Ashraf, Song Han, William J Dally and Kurt Keutxer, Squeezenet: AlexNet-level accuracy with 50x fewer parameters and 0.5mb model size, Arvix preprint: arvix:1602.00360, 2016

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa

Questions?

Supervisor: Dr. Jose AlvarezStudent: Kiarie Ndegwa