Adeia Semiconductor Bonding Technologies Inc.

Fotonation Limited

Fotonation Vision Limited

Adeia Imaging LLC

Tessera Technologies Ireland Limited

Digitaloptics Corporation Europe Limited

Mihnea Gangea

Petronel Bigioi

Stefan Petrescu

Eran Steinberg

Peter Corcoran

Alexandru Drimbarean

Alexei Pososin

Emilian David

Catalina Neghina

Eric L Zarakov

Florin Nanu

Adrian Zamfir

Mihai Ciuc

Adrian Dorin Capata

Gabriel Costache

Andrei Vasiliu

Mihai Ianculescu

Yury Prilutsky

Piotr Stec

Corneliu Zaharia

Constantin Vertan

George Susanu

Dan Filip

Szabolcs Fulop

Leo Blonk

Bogdan Sultana

Radu Nicolau

Vlad Ionut Ursachi

Erik Zarakov

Leendert Blonk

Alex Drimbarean

Stefan Mirel Petrescu

A method and system for detecting facial expressions in digital images and applications therefore are disclosed. Analysis of a digital image determines whether or not a smile and/or blink is present on a person's face. Face recognition, and/or a pose or illumination condition determination, permits application of a specific, relatively small classifier cascade.

Detecting facial expressions in digital images

A template matching module is configured to program a processor to apply multiple differently-tuned object detection classifier sets in parallel to a digital image to determine one or more of an object type, configuration, orientation, pose or illumination condition, and to dynamically switch between object detection templates to match a determined object type, configuration, orientation, pose, blur, exposure and/or directional illumination condition.

Face or other object detection including template matching

A digital camera has an integral flash and stores and displays a digital image. Under certain conditions, a flash photograph taken with the camera may result in a red-eye phenomenon due to a reflection within an eye of a subject of the photograph. A digital apparatus has a red-eye filter which analyzes at least one partial face region identified within the digital image for the red-eye phenomenon and modifies the image to eliminate the red-eye phenomenon by changing the red area to black. The modification of the image is enabled when a photograph is taken under conditions indicative of the red-eye phenomenon. The modification is subject to anti-falsing analysis which further examines the area around the red-eye area for indicia of the eye of the subject. The detection and correction can be optimized for performance and quality by operating on subsample versions of the image when appropriate.

Partial face detector red-eye filter method and apparatus

A method of detecting a face in an image includes performing face detection within a first window of the image at a first location. A confidence level is obtained from the face detection indicating a probability of the image including a face at or in the vicinity of the first location. Face detection is then performed within a second window at a second location, wherein the second location is determined based on the confidence level.

Face searching and detection in a digital image acquisition device

A method of skin segmentation of a digital image is operable in an acquisition device. An image is acquired. A value indicative of a redness of a pixel of said image is compared with a face skin pixel redness criterion. The pixel is identified as a face skin pixel if said criterion is satisfied.

Color segmentation

Faces in images are quickly detected with minimal memory resource usage. Instead of calculating a Haar-like feature value by subtracting the average pixel intensity value in one rectangular region from the average pixel intensity value in another, adjacent rectangular region, a face-detection system calculates that Haar-like feature value by dividing the average pixel intensity value in one rectangular region by the average pixel intensity value in another adjacent rectangular region. Thus, each Haar-like value is calculated as a ratio of average pixel intensity values rather than as a difference between such average pixel intensity values. The feature values are calculated using this ratio-based technique both during the machine-learning procedure, in which the numerical ranges for features in known face-containing images are learned based on labeled training data, and during the classifier-applying procedure, in which an unlabeled image's feature values are calculated and compared to the previously machine-learned numerical ranges.

Face detection using division-generated Haar-like features for illumination invariance

Faces in images are quickly detected with minimal memory resource usage. Instead of calculating a Haar-like feature value by subtracting the average pixel intensity value in one rectangular region from the average pixel intensity value in another, adjacent rectangular region, a face-detection system calculates that Haar-like feature value by dividing the average pixel intensity value in one such rectangular region by the average pixel intensity value in the other such adjacent rectangular region. Thus, each Haar-like value is calculated as a ratio of average pixel intensity values rather than as a difference between such average pixel intensity values. The feature values are calculated using this ratio-based technique both during the machine-learning procedure, in which the numerical ranges for features in known face-containing images are learned based on labeled training data, and during the classifier-applying procedure, in which an unlabeled image's feature values are calculated and compared to the previously machine-learned numerical ranges.

Face detection using division-generated haar-like features for illumination invariance

An image acquisition device having a wide field of view includes a lens and image sensor configured to capture an original wide field of view (WFoV) image with a field of view of more than 90&#xb0;. The device has an object detection engine that includes one or more cascades of object classifiers, e.g., face classifiers. A WFoV correction engine may apply rectilinear and/or cylindrical projections to pixels of the WFoV image, and/or non-linear, rectilinear and/or cylindrical lens elements or lens portions serve to prevent and/or correct distortion within the original WFoV image. One or more objects located within the original and/or distortion-corrected WFoV image is/are detectable by the object detection engine upon application of the one or more cascades of object classifiers.

Object detection and rendering for wide field of view (WFOV) image acquisition systems

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Bucharest, Romania

Mihnea Gangea