Supervised descent method and its applications to face alignment

CVPR 20132014/08/26

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Authors

Xuehan Xiong, Fernando DelaTorre

F(x): Non-linear function Y: known vector X: motion parameters (landmark later)

Regression

X: landmark location: 2p x 1 p: num of landmark

R: descent direction: 2p x 128p O: SIFT features: 128p x 1 b: bias: 2p x 1 RO = X, 2px128p x 128px1 = 2px1

SIFT features

4 x 4 array (16 x 16 sample array) 8 orientation bins 4 x 4 x 8 = 128

Learning

Learn: Rk, bk

R: descent direction: 2p x 128p O: SIFT features: 128p x m dx: 2p x m, b: bias: 2p x m m: num of images

Learning – implementation

R: descent direction 2p x 128p O: SIFT features: 128p x m dx: 2p x m, b: bias: 2p x m m: num of images

Solve with least square method: Ax’ = B A: O^t: m x 128p x’: R^t: 128p x 2p B: dx^t: m x 2p

Matrix formDescription

“Direction” Ground truthBias

Description

“Direction” + bias Ground truth

1

Learning – recursive

Learn: Rk, bk

New {dX, O} New Rk, bk

Questions

Num of iteration?

Thx.