Local Phase Quantization Features Extraction in Discriminative Subspace for Kinship Verification Oualid Laiadi∗ , Abdelmalik Ouamane† , Abdelhamid Benakcha‡ , Abdelmalik Taleb-Ahmed§ and Abdenour Hadid¶ ∗ Laboratory
of LESIA, University of Biskra, Algeria.
[email protected] of Biskra, Algeria.
[email protected] ‡ Laboratory of LGEB, University of Biskra, Algeria.
[email protected] § LAMIH, UMR CNRS 8201 UVHC, University of Valenciennes, France
[email protected] ¶ Center for Machine Vision Research, PO Box 4500, FI-90014 University of Oulu, Finland.
[email protected] † University
Abstract—We introduce LPQ-SIEDA approach to address the challenges posed in kinship verification from face images. Many experiments on the benchmark databases, namely the Cornell database and the UB Kinface database. More substantially, unlike most of the previous works cited in the literature, our proposed approach attain stable performance on all benchmark databases. Our results elucidate that using the proposed appraoch shows high performance compared favorably against the recent approaches in the literature on the benchmark databases. Keywords—kinship verification, face images, LPQ-SIEDA approach.
I.
I NTRODUCTION
Nowadays, biometric applications are numerous from facial uses and show a best performance in different face challenging such as face recognition [1], [2], facial demographic estimation [3], kinship verification [4] and so on. Kinship verification is an emerging topic research in vision computering that is attract the attention of the research community. The verification of kinship relationships through facial images intend the possibility of automatically checking if two persons come from the same family or not. The challenge of automatic kinship verification is to learn and automatically extract the similarity between family members. For example, the entries could be two faces (Face A and Face B) and the result could be a decision if Person A and Person B are a kin or non-kin. This has many potential applications such as creation of family trees, family album organization, image annotation, finding missing children and forensic. Automatic kinship verification is inevitably deal with facial images of various ages and ethnicities, captured under uncontrolled environments and without any restriction in terms of pose, lighting, background, expression, and partial occlusion. Moreover, the Parent-Child relation contains four kinds of kin relations such as Father-Son, Mother-Son, Father-Daughter, Mother-Daughter, these relations put an another difficulty because the pair of given images can be from persons of different gender and with a different ages. Further, the face attributes which relating to persons of the same family may show a large dissimilarity whereas pair faces of persons with no kinship may look like similar. The paper is organized as follows: Our proposed method for face kin verification is described in Section II. The experimental data and setup are presented and results are discussed
in Section III. Finally, concluding remarks are given in Section IV. II.
O UR APPROACH
We aim to investigate the effectiveness of our approach by extracting face representations from Local Phase Quantization (LPQ) texture descriptor. Figure 1 shows our kinship verification scheme. The Parent-Child pair of face images is given as an input. First, the pair face images is cropped and normalized into an X × Y pixels. Then, we convert each pair of face images from (RGB) into grayscale space (i.e. Gray). Then, we extract the local features from each face iamge using Local Phase Quantization descriptor. The encoded images are divided into K non-overlapping rectangular patches and each patch is summarized by a histogram. The histograms of different patches are concatenated to form a high dimensional feature vector. The features are then projected into (SIEDA) subspace. We compute the cosine similarity between the projected feature vectors of the pair face images. Finally, the scores is compared to a threshold, set from the receiver operating characteristic (ROC) curve during performance evaluation, to decide whether the pair belongs to the persons from the same family or not. In the following we provide the details of the steps of our approach. Kin
Parent SIEDA
Child
Cosine Similarity
Non-Kin SIEDA
Input images
Image gray conversion
Local Phase Quatization
Subspace reduction and classi!cation
Fig. 1: Our proposed approach.
A. Local Phase Quantization (LPQ) The LPQ descriptor was proposed by [5] to be accord with fuzzy images. By a Short Term Fourier Transform (STFT), the LPQ utilize the sectional phase information derived to analyse the M × M neighbourhoods framing of examined pixel x. Let Fn (x) be the output of the STFT at the pixel
x using the bi-dimensional spatial frequency n. In the LPQ descriptor, only four complex frequencies are considered: n0 = (α, 0), n1 = (α, α), n2 = (0, α), n3 = (α, α) where α is a small scalar frequency (α
