Monthly Archives: November 2015

Age and Gender Classification using Deep Convolutional Neural Networks

In the last few posts we mostly talked about binary image descriptors and the previous post in this line of works described our very own LATCH descriptor [1] and presented an evaluation of various binary and floating point image descriptors. In the current post we will shift our attention to the field of Deep Learning and present our work on Age and Gender classification from face image using Deep Convolutional Neural Networks [2].

Example images from the AdienceFaces benchmark

Example images from the AdienceFaces benchmark

Our method was presented in the following paper:

Gil Levi and Tal Hassner, Age and Gender Classification using Convolutional Neural Networks, IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, June 2015.

For code, models and examples, please see our project page.

New! Tensor-Flow implementation of our method .

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Performance Evaluation of Binary Descriptors – Introducing the LATCH descriptor

In the last few posts we introduced the concept of local image descriptors and specifically binary image descriptors. We surveyed notable example of binary descriptors, namely BRIEF[1], ORB[2], BRISK [3] and FREAK[4]. Here, we will both introduce a novel binary descriptor that we have developed and give a full evaluation of several binary and floating point descriptors. We will show that our proposed descriptor – the LATCH descriptor[5] – outperforms the alternatives with similar running times. We will also demonstrate its performance in the real world application of 3D reconstruction from multiple images.

Given an image patch centered around a keypoint, LATCH compares the intensity of three pixel patches in order to produce a single bit in the final binary string representing the patch. Example triplets are drawn over the patch in green and blue

Given an image patch centered around a keypoint, LATCH compares the intensity of three pixel patches in order to produce a single bit in the final binary string representing the patch. Example triplets are drawn over the patch in green and blue

Our proposed LATCH descriptor was presented in the following paper:

Gil Levi and Tal Hassner, LATCH: Learned Arrangements of Three Patch Codes, IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, March, 2016

Here is a short video of me presenting LATCH at the WACV 16 conference (I apologize for the technical problems in the video).

Our LATCH descriptor  has already been officially integrated into OpenCV3.0 and has even won the CVPR 2015, OpenCV State of the Art Vision Challenge, in the Image Registration category !

Also, see CUDA (GPU) implementation of the LATCH descriptor and a cool visual odometry demo, both by Christopher Parker.

 

For more information, please see LATCH project page.

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