Adeia Semiconductor Bonding Technologies Inc.

Fotonation Limited

Fotonation Vision Limited

Adeia Imaging LLC

Fotonation Ireland Limited

Tessera Technologies Ireland Limited

Stefan Petrescu

Petronel Bigioi

Peter Corcoran

Eran Steinberg

Alexandru Drimbarean

Florin Nanu

Mihnea Gangea

Alexei Pososin

Adrian Dorin Capata

Mihai Ciuc

Emilian David

Adrian Zamfir

Catalina Neghina

Eric L Zarakov

Emanuela Haller

Florin Oprea

Gabriel Costache

Constantin Vertan

Andrei Vasiliu

Mihai Ianculescu

Corneliu Zaharia

George Susanu

Szabolcs Fulop

Alexandru Nicolaescu

Iulian Palade

Bogdan Sultana

Radu Nicolau

Vlad Ionut Ursachi

Alexandru Malaescu

Petronel Biglol

Erik Zarakov

Petronel Bigiol

Alex Drimbarean

Adrian Capata

Adam Capata

Petronelnel Bigioi

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

Related methods are provided for establishing a baseline value to represent an eyelid opening dimension for a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function; and for operating a system for monitoring eyelid opening values with real time video data.

Human monitoring system incorporating calibration methodology

Tracking units for facial features with advanced training for natural rendering of human faces in real-time are provided. An example device receives a video stream, and upon detecting a visual face, selects a 3D model from a comprehensive set of head orientation classes. The device determines modifications to the selected 3D model to describe the face, then projects a 2D model of tracking points of facial features based on the 3D model, and controls, actuates, or animates hardware based on the facial features tracking points. The device can switch among an example comprehensive set of 35 different head orientation classes for each video frame, based on suggestions computed from a previous video frame or from yaw and pitch angles of the visual head orientation. Each class of the comprehensive set is trained separately based on a respective collection of automatically marked images for that head orientation class.

Facial features tracker with advanced training for natural rendering of human faces in real-time

Method for monitoring eyelid opening values. In one embodiment video image data is acquired with a camera which data are representative of a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function. When the person's head undergoes a change in yaw angle, such that eyelids of both eyes of the person are captured with the camera, but one eye is closer to the camera than the other eye, a weighting factor is applied, which factor varies as a function of the yaw angle such that a value representative of eyelid opening data based on both eyes is calculated.

A method of processing an image comprises: acquiring an image of a scene including an object having a recognizable feature. A lens actuator setting providing a maximum sharpness for a region of the image including the object and a lens displacement corresponding to the lens actuator setting are determined. A distance to the object based on the lens displacement is calculated. A dimension of the feature as a function of the distance to the object, the imaged object size and a focal length of a lens assembly with which the image was acquired, is determined. The determined dimension of the feature is employed instead of an assumed dimension of the feature for subsequent processing of images of the scene including the object.

Image processing method

A method of making an image in a digital camera comprises capturing a digital image of a scene into which the camera user is to be inserted, and superimposing a symbol (subject locator) onto the scene image representing at least a part of a human subject. The subject locator is scaled to a desired size and moved to a desired position relative to the scene image. Next a digital image of the user is captured, and at least the part of the user image represented by the subject locator is extracted. The part of the user image represented by the subject locator is scaled (before or after extraction) to substantially the same size as the subject locator and inserted into the first image at the position of the subject locator. In a second embodiment the method comprises displaying a preview image of a scene into which the camera user is to be inserted, and superimposing the subject locator on the preview image. The subject locator is scaled to a desired size and moved to a desired position relative to the edges of the preview image. Face detection detects the camera user entering the scene displayed by the preview image. The preview image is scaled and panned to bring the part of the preview image represented by the subject locator to substantially the same size and position as the subject locator. Finally, a digital image of the scene is captured.

Method of making a digital camera image of a first scene with a superimposed second scene

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

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Bucharest, Romania

Stefan Petrescu