Carlos O Castillo

US Patent:

20190303754, Oct 3, 2019

Filed:

Mar 28, 2018

Appl. No.:

15/938898

Inventors:

- College Park MD, US
Carlos CASTILLO - Silver Spring MD, US
Ramalingam CHELLAPPA - Potomac MD, US

International Classification:

G06N 3/08
G06K 9/00
G06K 9/62
G06N 5/04
G06F 17/11

Abstract:

Various face discrimination systems may benefit from techniques for providing increased accuracy. For example, certain discriminative face verification systems can benefit from L-constrained softmax loss. A method can include applying an image of a face as an input to a deep convolutional neural network. The method can also include applying an output of a fully connected layer of the deep convolutional neural network to an L-normalizing layer. The method can further include determining softmax loss based on an output of the L-normalizing layer.

US Patent:

20190244014, Aug 8, 2019

Filed:

Oct 10, 2017

Appl. No.:

16/340859

Inventors:

- College Park MD, US
Swaminathan SANKARANARAYANAN - University Park MD, US
Carlos CASTILLO - Silver Spring MD, US
Ramalingam CHELLAPPA - Potomac MD, US

International Classification:

G06K 9/00
G06K 9/62

Abstract:

Various facial recognition systems may benefit from appropriate use of computer systems. For example, certain face analysis systems may benefit from an all-in-one convolutional neural network that has been appropriately configured. A method can include obtaining an image of a face. The method can also include processing the image of the face using a first set of convolutional network layers configured to perform subject-independent tasks. The method can further include subsequently processing the image of the face using a second set of convolutional network layers configured to perform subject-dependent tasks. The second set of convolutional network layers can be integrated with the first set of convolutional network layers to form a single convolutional neural network. The method can additionally include outputting facial image detection results based on the processing and subsequent processing.

US Patent:

20180211099, Jul 26, 2018

Filed:

Jul 20, 2016

Appl. No.:

15/746237

Inventors:

- College Park MD, US
Vishal M. PATEL - Silver Spring MD, US
Ramalingam CHELLAPPA - Potomac MD, US
Carlos D. CASTILLO - Silver Spring MD, US

International Classification:

G06K 9/00
G06K 9/62

Abstract:

Various image processing may benefit from the application deep convolutional neural networks. For example, a deep multi-task learning framework may assist face detection, for example when combined with landmark localization, pose estimation, and gender recognition. An apparatus can include a first module of at least three modules configured to generate class independent region proposals to provide a region. The apparatus can also include a second module of the at least three modules configured to classify the region as face or non-face using a multi-task analysis. The apparatus can further include a third module configured to perform post-processing on the classified region.