SARDEGNARICERCHE

Fusion of multiple clues for photo-attack detection in face recognition systems

Roberto Tronci1,2, Daniele Muntoni1,2, Gianluca Fadda1, Maurizio Pili1, Nicola Sirena1, Marco Ristori1, Gabriele Murgia1, Fabio Roli2

1Ambient Intelligence Lab, Sardinia DistrICT, Sardegna Ricerche, ITALY

2DIEE, Dept. Electric and Electronic Engineering, University of Cagliari, ITALY

{roberto.tronci, muntoni, fadda, maurizio.pili, sirena, gabriele.murgia, ristori}@sardegnaricerche.it{roberto.tronci, daniele.muntoni, roli}diee.unica.it

ClassificationOur approach

At classification stage scores are computed over a sliding window of a few seconds of video.

Within this window, static analysis results in FxN scores (F frames and N visual representations). A unique score is computed through a DSC algorithm. :

Finally, fusion between static and video analysis is performed as:

Introduction

Experimental results: the face spoof competitionFor our experiments we used the Print-Attack Replay Database developed for the IJCB 2011 Competition on counter measures to 2D facial spoofing attacks from the Idiap Research Institute.

Although static analysis alone easily achieves a perfect separation in the test set, we enhanced its classification with the video analysis in order to grant performances even with higher quality printed photos or high quality displays (smart-phones, tablets and other modern portable devices).

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We faced the problem of detecting 2-D face spoofing attacks performed by placing a printed photo of a real user in front of the camera.

For this type of attack it is not possible to relay just on the face movements as a clue of vitality because the attacker can easily simulate such a case, and also because real users often show a “low vitality” during the authentication session.

In this paper, we perform both video and static analysis in order to employ complementary information about motion, texture and liveness and consequently to obtain a more robust classification.

AmILab's Spoof Detector implements a multi-clue approach.

Static analysis tackles the visual characteristics of a photo attack.

The visual representations that we propose to use are: Color and Edge Directivity Descriptor, Fuzzy Color and Texture Histogram, MPEG-7 Descriptors (like Scalable Color and Edge Histogram), Gabor Texture, Tamura Texture, RGB and HSV Histograms, and JPEG Histogram.

For each frame, each of the above mentioned visual representations result in a specific score.

Video analysis aims to detect vitality clues. Clues examined in this work are motion analysis of the scene and the number of eye blinks that are represented by two independent scores.

IJCB2011IJCB2011

Ambient Intelligence Lab - Edificio 1, Loc. Piscinamanna, 09010 Pula (CA), Italy - Tel. +39 070 9243 2682

http://prag.diee.unica.it/amilab/ labiam@sardegnaricerche.it

Introduction of video analysis results in lower performances in terms of separation of scores' distributions. However, the proposed fusion scheme still proved to be very effective and robust.

The contribution of video analysis in terms of robust classification will be further investigated in future works.

LOW?

Still Frame Characteristic analysis

Global motion

Blink detection

Yes

DSC

S sa = 1−⋅min{S i , f }⋅max {S i , f } i∈[1,N ] , f ∈[1, F ]

S = { ⋅S sa1−⋅S bl , if Sm is high1⋅S sa2⋅S bl 3⋅Sm , if Sm is low

Contacts

S bl

S sa

Sm

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