Deep learning for face detection in mobile device

Question

I want to create a face detection mobile app and I want to do it with a regular Deep Learning(Convolutional Network). I will train it with my computer and use trained data in the mobile app.

My question is that: can I get very fast computation in the regulat phone like iPhone? I need it be very fast and under 1 sec can detect a face in the video. Is it possible on a mobile device? or this kind of task need more powerful hardware?

I know training phase must be in a powerful computer but I mean production phase in a mobile device.

for example, if I put my phone in a street, It can detect all peoples face with the same deep network in training phase?

Answer 1

Yes, this is possible, but not with standard CNN architectures, some changes are needed:

• One approach is CNNs with binary weights, so evaluating the CNN can just be done with bit operations. There are many publications about this, like this, this or this. I have seen an implementation of YOLO with binary weights running in real-time on an iPhone, so it is definitely possible.
• A second approach is to reduce the number of parameters of the neural network, for example if you train a network with 5000 weights and gets detection performance that is close to what you want, then this network might run in real-time. But this is harder.
• Third approach is just to optimize the neural network architecture to minimize parameters, and combine it with a very optimized implementation. There are algorithms to speedup convolution operations, such as L-CNN, or the ones implemented by cuDNN.

A very good related resource are the presentation and papers from the The 1st International Workshop on Efficient Methods for Deep Neural Networks.