Extraction and fusion of partial face features for cancelable identity verification

Oh, Beom-Seok; Toh, Kar-Ann; Choi, Kwontaeg; Teoh, Andrew Beng Jin; Kim, Jaihie

doi:10.1016/j.patcog.2012.02.027

Cited by 19 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(i.e., image taken from database first printed on a paper using a printer and then scanned to generate the templates). The extraction of horizontal and vertical partial face image provide better performance than using global face images, because partial images helps in extracting local face features which are less sensitive to imaging conditions than those using global appearance based features [3]. Applying LGIP approach after feature extraction binarizes the features and makes the features more robust under different illuminations and noise.…”

Section: Discussionmentioning

confidence: 99%

“…This step extracts the face features from different portions of the face using projected random matrices as described in [3]. Initially, the face XR qXp (where q is height and p is width in pixels of face shown in Fig.…”

Section: Partial Face Features Extractionmentioning

confidence: 99%

“…R 1 R kÂp is a two dimensional matrix consisting of a sequence of the binary values "0" and "1", where k is an arbitrary number representing the height of the projection matrices and p is width of the face image. Based on a previous work [3], the height of the projection matrices (k) in this work is set equal to 5. The number of ones in each row is arbitrary.…”

Section: Partial Face Features Extractionmentioning

confidence: 99%

“…The number of ones in each row is arbitrary. We assumed a series of 15 ones for each row [3]. This matrix R 1 is used to extract vertical features from the sub-image X with the same definition for R 2 R qÂk , which will be post-multiplied with the sub-image X, to get horizontal features.…”

Section: Partial Face Features Extractionmentioning

confidence: 99%

“…The proposed method can be more robust against identity forgery by using different projection matrix to generate the binary vectors for individual users or documents. In case of document-specific projection matrix, a new ID document can be issued by [3] selecting another projection matrix. It is to be noted that, the projection matrices can be interpreted as a random seed that is used to encode features.…”

Section: Partial Face Features Extractionmentioning

confidence: 99%

See 4 more Smart Citations

Robust 2D Face Recognition Under Different Illuminations Using Binarized Partial Face Features: Towards Protecting ID Documents

Butt

Alkhatib

2015

Computational Forensics

View full text Add to dashboard Cite

Abstract. Biometric recognition techniques have been widely employed in numerous applications, such as access control, identity check etc. In this paper we propose a method for protecting personal identity documents against forgery by using 2D face image on the ID document. The main components of this method includes detection of the face image from the document, extracting features from partial face images and converting the extracted features to binary feature vectors using Local Gradient Increasing Pattern (LGIP) approach. The binary feature vectors are concatenated to form a binary template which can be encoded and stored on the ID document in the form of a 2D bar code. This 2D bar code will be used to authenticate the ownership of the ID document. The face recognition method is evaluated using FRGC and FERET databases. The results show that this method can efficiently authenticate an ID document on the basis of the face image and the method can also be used to retrieve a subject from a database of images. The method is proved to be robust even if an ID document is scanned under different illuminations conditions. A report on document authentication where the face image is damaged is also presented.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Partial Face Features Extractionmentioning

confidence: 99%

Section: Partial Face Features Extractionmentioning

confidence: 99%

Section: Partial Face Features Extractionmentioning

confidence: 99%

Section: Partial Face Features Extractionmentioning

confidence: 99%

See 3 more Smart Citations