Self-similarity representation of Weber faces for kinship classification

doi:10.1109/BTAS.2012.6374584

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Self-similarity representation of Weber faces for kinship classification

Proceedings Article•DOI•

Self-similarity representation of Weber faces for kinship classification

Naman Kohli¹, Richa Singh¹, Mayank Vatsa¹•Institutions (1)

Indraprastha Institute of Information Technology¹

06 Dec 2012-pp 245-250

TL;DR: A kinship classification algorithm that uses the local description of the pre-processed Weber face image to outperforms an existing algorithm and yields a classification accuracy of 75.2%.

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Abstract: Establishing kinship using images can be utilized as context information in different applications including face recognition. However, the process of automatically detecting kinship in facial images is a challenging and relatively less explored task. The reason for this includes limited availability of datasets as well as the inherent variations amongst kins. This paper presents a kinship classification algorithm that uses the local description of the pre-processed Weber face image. A kinship database is also prepared that contains images pertaining to 272 kin pairs. The database includes images of celebrities (and their kins) and has four ethnicity groups and seven kinship groups. The proposed algorithm outperforms an existing algorithm and yields a classification accuracy of 75.2%.

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Journal Article•DOI•

Automated kinship verification and identification through human facial images: a survey

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Mohammed Almuashi¹, Siti Zaiton Mohd Hashim¹, Dzulkifli Mohamad¹, Mohammed Hazim Alkawaz², Aida Ali¹ - Show less +1 more•Institutions (2)

Universiti Teknologi Malaysia¹, Management and Science University²

01 Jan 2017-Multimedia Tools and Applications

TL;DR: It is found that Deep Learning (DL) has mostly outperformed numerous methods using manually designed features by automatically learning and extracting important information from facial features, and enable significant visual recognition functions by improving accuracy in most applications.

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Abstract: Face is the most considerable constituent that people use to recognize one another. Humans can quickly and easily identify each other by their faces and since facial features are unobtrusive to lighting condition and pose, face remains as a dynamic recognition approach to human. Kinship recognition refers to the task of training a machine to recognize the blood relation between a pair of kin and non-kin faces (verification) based on features extracted from facial images, and to determine the exact type or degree of that relation (identification). Automatic kinship verification and identification is an interesting areas for investigation, and it has a significant impact in many real world applications, for instance, forensic, finding missing family members, and historical and genealogical research. However, kinship recognition is still not largely explored due to insufficient database availability. In this paper we present a survey on issues and challenges in kinship verification and identification, related previous works, current trends and advancements in kinship recognition, and potential applications and research direction for the future. We also found that Deep Learning (DL) has mostly outperformed numerous methods using manually designed features by automatically learning and extracting important information from facial features, and enable significant visual recognition functions by improving accuracy in most applications.

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28 citations

Cites background or methods or result from "Self-similarity representation of W..."

...According to [35], three factors are used in deriving statistics, namely, features or methods, degree of kinship, and age difference....
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...In addition, the extent and point of similarities (kin can have similar eyes, nose shape, and forehead) vary from person to person, which causes difficulty in establishing kinship using facial images only [35]....
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...In case of gender, the results of previous experiments [29, 35, 77, 83] show that the percentage of accuracy increases when the father is compared with his son and the mother with her daughter; this percentage decreases if the comparison is between the father and his daughter and the mother and her son....
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...Many previous studies focused on developing an automatic approach of kinship based on genetic relatedness, such as parent-child, which represent four types of relationships (fatherson, father-daughter, mother-son, and mother-daughter) [43, 69, 77], sibling pairs [4, 70], and parent-child and siblings, which represent seven types of relationships (father-son, mother-son, father-daughter, mother-daughter, brother-sister, brother-brother, and sistersister) [22, 35] to support this claim....
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...3 % [35], was obtained using salient or key points and difference of Gaussian (DoG) features in the UB KinFace database [35]....
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Journal Article•DOI•

Kinship Measurement on Face Images by Structured Similarity Fusion

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Min Xu¹, Yuanyuan Shang¹•Institutions (1)

Capital Normal University¹

05 Dec 2016-IEEE Access

TL;DR: This paper proposes a new similarity metric learning method for kinship measurement on human faces that achieves competitive or better accuracy performance in comparison with the state-of-the-art multimetric learning-based kinship verification methods but enjoys the superiority in computational efficiency, making it more practical for vision-based kin measurement applications.

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Abstract: Kinship verification via facial images is an emerging problem in computer vision and biometrics. Recent research has shown that learning a kin similarity measurement plays a critical role in constructing a vision-based kinship measurement system. We propose in this paper a new similarity metric learning method for kinship measurement on human faces. To this end, we first extract multiple feature representations for each face image using different face descriptors. Then, multiple sparse bilinear similarity models (one for each view) are jointly learned by using joint structured sparsity-inducing norms, such that the similarity score of a pair of child-parent images is consistently higher than those of the pairs without kinship relations while leveraging the interactions and correlations among the multiview data to obtain the fused and higher level information. We also derive an efficient algorithm to solve the formulated nonsmooth objective. Experimental results on kinship data sets show that our method achieves competitive or better accuracy performance in comparison with the state-of-the-art multimetric learning-based kinship verification methods but enjoys the superiority in computational efficiency, making it more practical for vision-based kin measurement applications.

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27 citations

Cites background from "Self-similarity representation of W..."

...Existingmethods for kinship verification are either featurebased [3]–[5], [7], [8], [11], [13], [40], [41], [44] or modelbased [6], [9], [10], [12], [43]....
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...Motivated by this, kinship verification via facial images has attracted more attention from pattern recognition and biometrics society [3]–[13], [36], [37], [40]–[43]....
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Proceedings Article•DOI•

Visual Transformation Aided Contrastive Learning for Video-Based Kinship Verification

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Hamdi Dibeklioglu¹•Institutions (1)

Bilkent University¹

01 Oct 2017

TL;DR: A Siamese-like coupled convolutional encoder-decoder network is proposed to reveal resemblance patterns of kinship while discarding the similarity patterns that can also be observed between people who do not have a kin relationship.

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Abstract: Automatic kinship verification from facial information is a relatively new and open research problem in computer vision. This paper explores the possibility of learning an efficient facial representation for video-based kinship verification by exploiting the visual transformation between facial appearance of kin pairs. To this end, a Siamese-like coupled convolutional encoder-decoder network is proposed. To reveal resemblance patterns of kinship while discarding the similarity patterns that can also be observed between people who do not have a kin relationship, a novel contrastive loss function is defined in the visual appearance space. For further optimization, the learned representation is fine-tuned using a feature-based contrastive loss. An expression matching procedure is employed in the model to minimize the negative influence of expression differences between kin pairs. Each kin video is analyzed by a sliding temporal window to leverage short-term facial dynamics. The effectiveness of the proposed method is assessed on seven different kin relationships using smile videos of kin pairs. On the average, 93:65% verification accuracy is achieved, improving the state of the art.

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25 citations

Cites background from "Self-similarity representation of W..."

...Then, several studies have aimed to engineer powerful facial appearance representations such as Spatial Pyramid LEarningbased descriptors [38], DAISY descriptors [12], Gaborbased Gradient Orientation Pyramid [39], Self Similarity Representation [16], semantic-related attributes [32], SIFT flow based genetic Fisher vector feature [26], etc....
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Journal Article•DOI•

A literature survey on kinship verification through facial images

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Xiaoqian Qin, Dakun Liu, Dong Wang¹•Institutions (1)

Agency for Science, Technology and Research¹

15 Feb 2020-Neurocomputing

TL;DR: This paper attaches great importance to the difficulties in practical applications of kinship verification, and reviews the prominent algorithms from the perspective of learning more efficient models with more diverse kin relations, and shows how to develop an efficient and robust kinships verification system.

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24 citations

Journal Article•DOI•

Patch-Based Dual-Tree Complex Wavelet Transform for Kinship Recognition

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Aarti Goyal¹, Toshanlal Meenpal¹•Institutions (1)

National Institute of Technology, Raipur¹

01 Jan 2021-IEEE Transactions on Image Processing

TL;DR: Novel patch-based kinship recognition methods based on dual-tree complex wavelet transform (DT-CWT) and Selective Patch-Based DT-C WT are presented, which achieves competitive accuracy to state-of-the-art methods and representative patches contribute more similarities in parent/child image pairs and improve kinship accuracy.

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Abstract: Kinship recognition is a prominent research aiming to find if kinship relation exists between two different individuals. In general, child closely resembles his/her parents more than others based on facial similarities. These similarities are due to genetically inherited facial features that a child shares with his/her parents. Most existing researches in kinship recognition focus on full facial images to find these kinship similarities. This paper first presents kinship recognition for similar full facial images using proposed Global-based dual-tree complex wavelet transform (G-DTCWT). We then present novel patch-based kinship recognition methods based on dual-tree complex wavelet transform (DT-CWT): Local Patch-based DT-CWT (LP-DTCWT) and Selective Patch-Based DT-CWT (SP-DTCWT). LP-DTCWT extracts coefficients for smaller facial patches for kinship recognition. SP-DTCWT is an extension to LP-DTCWT and extracts coefficients only for representative patches with similarity scores above a normalized cumulative threshold. This threshold is computed by a novel patch selection process. These representative patches contribute more similarities in parent/child image pairs and improve kinship accuracy. Proposed methods are extensively evaluated on different publicly available kinship datasets to validate kinship accuracy. Experimental results showcase efficacy of proposed methods on all kinship datasets. SP-DTCWT achieves competitive accuracy to state-of-the-art methods. Mean kinship accuracy of SP-DTCWT is 95.85% on baseline KinFaceW-I and 95.30% on KinFaceW-II datasets. Further, SP-DTCWT achieves the state-of-the-art accuracy of 80.49% on the largest kinship dataset, Families In the Wild (FIW).

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23 citations

Cites methods from "Self-similarity representation of W..."

...[36] proposed kinship recognition using preprocessed weber facial images by applying self similarity feature descriptor....
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Navneet Dalal¹, Bill Triggs¹•Institutions (1)

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31,952 citations

"Self-similarity representation of W..." refers background in this paper

...75% which was comparatively better than Local Binary Pattern (LBP) [1], Histogram of Gradients (HOG) [4], Principal Component Analysis (PCA) [8], and Linear Embedding (LE) [2]....
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"Self-similarity representation of W..." refers methods in this paper

...The χ(2) distance measures (in a vector form) are provided as input to the SVM classifier [11] with the classes being kin and non-kin....
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Proceedings Article•DOI•

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Abstract: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

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...The face region present in the image is first extracted using the Adaboost face detector [12]....
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Abstract: Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.

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...Therefore, Difference of Gaussian (DoG) approach [7] has been applied to extract these features using the steps below....
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Journal Article•DOI•

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University of Oulu¹

01 Dec 2006-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.

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Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

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"Self-similarity representation of W..." refers background in this paper

...75% which was comparatively better than Local Binary Pattern (LBP) [1], Histogram of Gradients (HOG) [4], Principal Component Analysis (PCA) [8], and Linear Embedding (LE) [2]....
[...]
...The authors reported an accuracy of 67.75% which was comparatively better than Local Binary Pattern (LBP) [1], Histogram of Gradients (HOG) [4], Principal Component Analysis (PCA) [8], and Linear Embedding (LE) [2]....
[...]