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)

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)

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Winona Daily News

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