1,379 research outputs found
Side-View Face Recognition
Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face recognition techniques to be used in house safety applications. Our aim is to recognize people as they pass through a door, and estimate their location in the house. Here, we compare available databases appropriate for this task, and review current methods for profile face recognition
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
Facial expression recognition in dynamic sequences: An integrated approach
Automatic facial expression analysis aims to analyse human facial expressions and classify them into discrete categories. Methods based on existing
work are reliant on extracting information from video sequences and employ either some form of subjective thresholding of dynamic information or
attempt to identify the particular individual frames in which the expected
behaviour occurs. These methods are inefficient as they require either additional subjective information, tedious manual work or fail to take advantage
of the information contained in the dynamic signature from facial movements
for the task of expression recognition.
In this paper, a novel framework is proposed for automatic facial expression analysis which extracts salient information from video sequences
but does not rely on any subjective preprocessing or additional user-supplied
information to select frames with peak expressions. The experimental framework demonstrates that the proposed method outperforms static expression
recognition systems in terms of recognition rate. The approach does not rely on action units (AUs) and therefore, eliminates errors which are otherwise
propagated to the final result due to incorrect initial identification of AUs.
The proposed framework explores a parametric space of over 300 dimensions
and is tested with six state-of-the-art machine learning techniques. Such
robust and extensive experimentation provides an important foundation for
the assessment of the performance for future work. A further contribution
of the paper is offered in the form of a user study. This was conducted in
order to investigate the correlation between human cognitive systems and the
proposed framework for the understanding of human emotion classification
and the reliability of public databases
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
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