Efficient Iris Spoof Detection via Boosted Local Binary Patterns

Abstract

Abstract. Recently, spoof detection has become an important and challenging topic in iris recognition. Based on the textural differences between the counterfeit iris images and the live iris images, we propose an efficient method to tackle this problem. Firstly, the normalized iris image is divided into sub-regions according to the properties of iris textures. Local binary patterns (LBP) are then adopted for texture representation of each sub-region. Finally, Adaboost learning is performed to select the most discriminative LBP features for spoof detection. In particular, a kernel density estimation scheme is proposed to complement the insufficiency of counterfeit iris images during Adaboost training. The comparison experiments indicate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.

Contact lens detection in iris images

“…LBP texture feature based algorithms have shown to be effective in cosmetic contact lens detection [21,29,59], thus we adopted multi-resolution LBP [21,29] benefits of the proposed pre-processing approach and the BSIF based iris texture description. In our experiments, the multi-resolution representation was extracted by applying LBP u2 P,R operator with eight sampling points (P = 8) at multiple scales (radii R) and concatenating the resulting LBP histograms into a single feature vector.…”

“…multiple layers. Consequently, by far the most common and promising approach has been to apply different local descriptors for cosmetic contact lens detection, including gray level co-occurrence matrix (GLCM) based features [28], a combination of GLCM features and iris-textons [55], multi-resolution local binary patterns (LBP) [21,29], weighted-LBP [59], scaleinvariant feature transform (SIFT) based hierarchical visual codebook [53], and binarized statistical image features (BSIF) [20,36]. Most of these works are reporting excellent detection rates very close to 100% in controlled scenarios but novel printed lens texture patterns (not seen during training) and sensor interoperability can yield to dramatic decrease in system performance [22,59].…”

“…Traditionally iris images are normalized into polar coordinate system also when detecting counterfeit irises [21,28,29,56,53,55]. However, the geometric transformation causes severe distortion on the regular lens texture patterns.…”

Spoofing Detection for Fingerprint using Hog and Iris using Surf Descriptor

“…Daugman [26] has used multi scale quadrature wavelets to extract texture phase structure information of the iris in order to generate a 2048 bit iris code and compared the difference between a pair of iris representations by computing their Hamming distance via the XOR. In 2009 [27] proposes the use of LBP to detect iris spoofing based on contract lens, where the iris is divided into six sub regions, these are rectified and LBPs are extracted at various scales. And finally the Adaboost algorithm is used to learn the most discriminative regional LBP features .…”

Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition

“…We have addressed many issues in (non-cooperative) iris recognition such as image quality evaluation [5], iris segmentation [6], feature extraction and classification [7][8][9], coarse iris classification [10], iris spoof detection [11] and iris image synthesis [12]. Regarding the hardware design, particular attention has also been paid on non-cooperative iris recognition.…”