A study of deep learning-based face recognition models for sibling identification

YesAccurate identification of siblings through face recognition is a challenging task. This is predominantly because of the high degree of similarities among the faces of siblings. In this study, we investigate the use of state-of-the-art deep learning face recognition models to evaluate their capacity for discrimination between sibling faces using various similarity indices. The specific models examined for this purpose are FaceNet, VGGFace, VGG16, and VGG19. For each pair of images provided, the embeddings have been calculated using the chosen deep learning model. Five standard similarity measures, namely, cosine similarity, Euclidean distance, structured similarity, Manhattan distance, and Minkowski distance, are used to classify images looking for their identity on the threshold defined for each of the similarity measures. The accuracy, precision, and misclassification rate of each model are calculated using standard confusion matrices. Four different experimental datasets for full-frontal-face, eyes, nose, and forehead of sibling pairs are constructed using publicly available HQf subset of the SiblingDB database. The experimental results show that the accuracy of the chosen deep learning models to distinguish siblings based on the full-frontal-face and cropped face areas vary based on the face area compared. It is observed that VGGFace is best while comparing the full-frontal-face and eyes—the accuracy of classification being with more than 95% in this case. However, its accuracy degrades significantly when the noses are compared, while FaceNet provides the best result for classification based on the nose. Similarly, VGG16 and VGG19 are not the best models for classification using the eyes, but these models provide favorable results when foreheads are compared

Image-based family verification in the wild

Facial image analysis has been an important subject of study in the communities of pat- tern recognition and computer vision. Facial images contain much information about the person they belong to: identity, age, gender, ethnicity, expression and many more. For that reason, the analysis of facial images has many applications in real world problems such as face recognition, age estimation, gender classification or facial expression recognition. Visual kinship recognition is a new research topic in the scope of facial image analysis. It is essential for many real-world applications. However, nowadays there exist only a few practical vision systems capable to handle such tasks. Hence, vision technology for kinship-based problems has not matured enough to be applied to real- world problems. This leads to a concern of unsatisfactory performance when attempted on real-world datasets. Kinship verification is to determine pairwise kin relations for a pair of given images. It can be viewed as a typical binary classification problem, i.e., a face pair is either related by kinship or it is not. Prior research works have addressed kinship types for which pre-existing datasets have provided images, annotations and a verification task protocol. Namely, father-son, father-daughter, mother-son and mother-daughter. The main objective of this Master work is the study and development of feature selection and fusion for the problem of family verification from facial images. To achieve this objective, there is a main tasks that can be addressed: perform a compara- tive study on face descriptors that include classic descriptors as well as deep descriptors. The main contributions of this Thesis work are: 1. Studying the state of the art of the problem of family verification in images. 2. Implementing and comparing several criteria that correspond to different face rep- resentations (Local Binary Patterns (LBP), Histogram Oriented Gradients (HOG), deep descriptors)

Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

LIPSNN: A Light Intrusion-Proving Siamese Neural Network Model for Facial Verification

Facial verification has experienced a breakthrough in recent years, not only due to the improvement in accuracy of the verification systems but also because of their increased use. One of the main reasons for this has been the appearance and use of new models of Deep Learning to address this problem. This extension in the use of facial verification has had a high impact due to the importance of its applications, especially on security, but the extension of its use could be significantly higher if the problem of the required complex calculations needed by the Deep Learning models, that usually need to be executed on machines with specialised hardware, were solved. That would allow the use of facial verification to be extended, making it possible to run this software on computers with low computing resources, such as Smartphones or tablets. To solve this problem, this paper presents the proposal of a new neural model, called Light Intrusion-Proving Siamese Neural Network, LIPSNN. This new light model, which is based on Siamese Neural Networks, is fully presented from the description of its two block architecture, going through its development, including its training with the well- known dataset Labeled Faces in the Wild, LFW; to its benchmarking with other traditional and deep learning models for facial verification in order to compare its performance for its use in low computing resources systems for facial recognition. For this comparison the attribute parameters, storage, accuracy and precision have been used, and from the results obtained it can be concluded that the LIPSNN can be an alternative to the existing models to solve the facet problem of running facial verification in low computing resource devices

A Survey on Metric Learning for Feature Vectors and Structured Data

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new method