We introduce a seemingly impossible task: given only an audio clip of someone speaking,
decide which of two face images is the speaker. In this paper we study this, and a number of …

We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal
biometric matching, in particular of voices and faces. Different from the existing methods …

In this paper, we study the associations between human faces and voices. Audiovisual
integration, specifically the integration of facial and vocal information is a well-researched …

Cross-modal biometric matching (CMBM) aims to determine the corresponding voice from a
face, or identify the corresponding face from a voice. Recently, many CMBM methods have …

How much can we infer about a person's looks from the way they speak? In this paper, we
study the task of reconstructing a facial image of a person from a short audio recording of …

This paper proposes a novel approach to extract meaningful content information from video
by collaborative integration of image understanding and natural language processing. As an …

We introduce the visual acoustic matching task, in which an audio clip is transformed to
sound like it was recorded in a target environment. Given an image of the target environment …

Recognizing faces in unconstrained videos is a task of mounting importance. While
obviously related to face recognition in still images, it has its own unique characteristics and …

It is broadly accepted that there is a" gender gap" in| face recognition accuracy, with females
having higher false| match and false non-match rates. However, relatively little is known …

Visible (VIS) to near infrared (NIR) face matching is a challenging problem due to the
significant domain discrepancy between the domains and a lack of sufficient data for training …