76 research outputs found
Facial Landmark Detection Evaluation on MOBIO Database
MOBIO is a bi-modal database that was captured almost exclusively on mobile
phones. It aims to improve research into deploying biometric techniques to
mobile devices. Research has been shown that face and speaker recognition can
be performed in a mobile environment. Facial landmark localization aims at
finding the coordinates of a set of pre-defined key points for 2D face images.
A facial landmark usually has specific semantic meaning, e.g. nose tip or eye
centre, which provides rich geometric information for other face analysis tasks
such as face recognition, emotion estimation and 3D face reconstruction. Pretty
much facial landmark detection methods adopt still face databases, such as
300W, AFW, AFLW, or COFW, for evaluation, but seldomly use mobile data. Our
work is first to perform facial landmark detection evaluation on the mobile
still data, i.e., face images from MOBIO database. About 20,600 face images
have been extracted from this audio-visual database and manually labeled with
22 landmarks as the groundtruth. Several state-of-the-art facial landmark
detection methods are adopted to evaluate their performance on these data. The
result shows that the data from MOBIO database is pretty challenging. This
database can be a new challenging one for facial landmark detection evaluation.Comment: 13 pages, 10 figure
Interspecies Knowledge Transfer for Facial Keypoint Detection
We present a method for localizing facial keypoints on animals by
transferring knowledge gained from human faces. Instead of directly finetuning
a network trained to detect keypoints on human faces to animal faces (which is
sub-optimal since human and animal faces can look quite different), we propose
to first adapt the animal images to the pre-trained human detection network by
correcting for the differences in animal and human face shape. We first find
the nearest human neighbors for each animal image using an unsupervised shape
matching method. We use these matches to train a thin plate spline warping
network to warp each animal face to look more human-like. The warping network
is then jointly finetuned with a pre-trained human facial keypoint detection
network using an animal dataset. We demonstrate state-of-the-art results on
both horse and sheep facial keypoint detection, and significant improvement
over simple finetuning, especially when training data is scarce. Additionally,
we present a new dataset with 3717 images with horse face and facial keypoint
annotations.Comment: CVPR 2017 Camera Read
Deep Learning Based Face Detection and Recognition in MWIR and Visible Bands
In non-favorable conditions for visible imaging like extreme illumination or nighttime, there is a need to collect images in other spectra, specifically infrared. Mid-Wave infrared (3-5 microm) images can be collected without giving away the location of the sensor in varying illumination conditions. There are many algorithms for face detection, face alignment, face recognition etc. proposed in visible band till date, while the research using MWIR images is highly limited. Face detection is an important pre-processing step for face recognition, which in turn is an important biometric modality. This thesis works towards bridging the gap between MWIR and visible spectrum through three contributions. First, a dual band based deep face detection model that works well in visible and MWIR spectrum is proposed using transfer learning. Different models are trained and tested extensively using visible and MWIR images and the one model that works well for this data is determined. For this model, experiments are conducted to learn the speed/accuracy trade-off. Following this, the available MWIR dataset is extended through augmentation using traditional methods and generative adversarial networks (GANs). Traditional methods used to augment the data are brightness adjustment, contrast enhancement, applying noise to and de-noising the images. A deep learning based GAN architecture is developed and is used to generate new face identities. The generated images are added to the original dataset and the face detection model developed earlier is once again trained and tested. The third contribution is the proposal of another GAN that converts given thermal ace images into their visible counterparts. A pre-trained model is used as discriminator for this purpose and is trained to classify the images as real and fake and an identity network is used to provide further feedback to the generator. The generated visible images are used as probe images and the original visible images are used as gallery images to perform face recognition experiments using a state-of-the-art visible-to-visible face recognition algorithm
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