How far can you get with a modern face recognition test set using only simple features?

In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously illustrated an inherent danger in using such sets, showing that an extremely basic recognition system, built on a trivial feature set, was able to take advantage of low-level regularities in popular object [10] and face [11] recognition sets, performing on par with many state-of-the-art systems. Recently, several groups have raised the performance “bar” for these sets, using more advanced classification tools. However, it is difficult to know whether these improvements are due to progress towards solving the core computational problem, or are due to further improvements in the exploitation of low-level regularities. Here, we show that even modest optimization of the simple model introduced by Pinto et al. using modern multiple kernel learning (MKL) techniques once again yields “state-of-th...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Nicolas Pinto, James J. DiCarlo, David D. Cox
• Keywords: Benchmark, Computer Vision, CVPR 2009, Face Recognition, Machine Learning, Multiple Kernel Learning, Object Recognition, V1, Visual Cortex