Karaaba, Mahir

Schomaker, Lambertus

Surinta, Olarik

Wiering, Marco

English

The Single Sample per Person Problem is a challengingproblem for face recognition algorithms. Patch-basedmethods have obtained some promising results for this problem.In this paper, we propose a new face recognition algorithm thatis based on a combination of different histograms of orientedgradients (HOG) which we call Multi-HOG. Each member ofMulti-HOG is a HOG patch that belongs to a grid structure.To recognize faces, we create a vector of distances computedby comparing train and test face images. After this, a distancecalculation method is employed to calculate the final distancevalue between a test and a reference image. We describe heretwo distance calculation methods: mean of minimum distances(MMD) and a multi-layer perceptron based distance (MLPD)method. To cope with aligning difficulties, we also proposeanother technique that finds the most similar regions for twocompared images. We call it the most similar region selectionalgorithm (MSRS). The regions found by MSRS are given tothe algorithms we proposed. Our results show that, while MMDand MLPD contribute to obtaining much higher accuracies thanthe use of a single histogram of oriented gradients, combiningthem with the most similar region selection algorithm results instate-of-the-art performances

ARTS repository - University of Groningen

Robust Face Recognition by Computing Distances from Multiple Histograms of Oriented Gradients

https://pure.rug.nl/ws/files/26826784/1570191637_Karaaba_CIBIM.pdf

Robust Face Recognition by Computing Distances from Multiple Histograms of Oriented Gradients

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ARTS repository - University of Groningen