Abstract
The Single Sample per Person Problem is a challenging
problem for face recognition algorithms. Patch-based
methods have obtained some promising results for this problem.
In this paper, we propose a new face recognition algorithm that
is based on a combination of different histograms of oriented
gradients (HOG) which we call Multi-HOG. Each member of
Multi-HOG is a HOG patch that belongs to a grid structure.
To recognize faces, we create a vector of distances computed
by comparing train and test face images. After this, a distance
calculation method is employed to calculate the final distance
value between a test and a reference image. We describe here
two distance calculation methods: mean of minimum distances
(MMD) and a multi-layer perceptron based distance (MLPD)
method. To cope with aligning difficulties, we also propose
another technique that finds the most similar regions for two
compared images. We call it the most similar region selection
algorithm (MSRS). The regions found by MSRS are given to
the algorithms we proposed. Our results show that, while MMD
and MLPD contribute to obtaining much higher accuracies than
the use of a single histogram of oriented gradients, combining
them with the most similar region selection algorithm results in
state-of-the-art performances.
problem for face recognition algorithms. Patch-based
methods have obtained some promising results for this problem.
In this paper, we propose a new face recognition algorithm that
is based on a combination of different histograms of oriented
gradients (HOG) which we call Multi-HOG. Each member of
Multi-HOG is a HOG patch that belongs to a grid structure.
To recognize faces, we create a vector of distances computed
by comparing train and test face images. After this, a distance
calculation method is employed to calculate the final distance
value between a test and a reference image. We describe here
two distance calculation methods: mean of minimum distances
(MMD) and a multi-layer perceptron based distance (MLPD)
method. To cope with aligning difficulties, we also propose
another technique that finds the most similar regions for two
compared images. We call it the most similar region selection
algorithm (MSRS). The regions found by MSRS are given to
the algorithms we proposed. Our results show that, while MMD
and MLPD contribute to obtaining much higher accuracies than
the use of a single histogram of oriented gradients, combining
them with the most similar region selection algorithm results in
state-of-the-art performances.
| Original language | English |
|---|---|
| Title of host publication | IEEE Symposium Series on Computational Intelligence |
| Subtitle of host publication | Symposium on Computational Intelligence in Biometrics and Identity Management |
| Publisher | IEEE (The Institute of Electrical and Electronics Engineers) |
| Number of pages | 7 |
| ISBN (Print) | 978-1-4799-7560-0 |
| Publication status | Published - 9-Dec-2015 |
Keywords
- face recognition
- machine learning