Head Pose Recommendation for Taking Good Selfies

Hsieh, Yi-Tsung; Yeh, Mei-Chen

doi:10.1145/3132515.3132518

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…The proposal results are resolved adaptively to the appearance and beginning posture of the information face. The client study demonstrates the suggestion execution of the framework is tolerably identified with the level of congruity among the picture takers' proposals [8]. Self-Trackam analysis video edges caught continuously to confine human faces in each edge.…”

Section: Literature Reviewmentioning

confidence: 94%

Recognition of Human Face Emotions Detection Using Computer Vision Based Smart Images

2021

IJATCSE

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Humans share a collection of basic and essential emotions that are expressed by facial expressions that seem to be consistent. The automated identification of humanemotion in imageswill be possible due to an algorithm that detects, extracts, and evaluates these facial expressions.The Face detector and recognizer application is a desktop application used to recognize human face emotions by using a computer vision based smart images. It consists of human face detection picture boxes and considers an image as an original image. It climaxes a face skin color, finds high impact area and identifies different emotions of the face from an image. The results of the image are depending upon the separation of Eyes & Lips movement of person. This is done by comparingface embedding vectors. It finds the smart photos focused on computer vision for successful identification of facial emotions in terms of various modes of speech such as smiling, shocking and weeping.

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Section: Literature Reviewmentioning

confidence: 94%

Recognition of Human Face Emotions Detection Using Computer Vision Based Smart Images

2021

IJATCSE

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“…The need to map the attention span of users has become a necessity in recent decades in almost all spheres of the industry being education [1] [2], medical [3][4], advertising [5], marketing [6], and many more. To face these real challenges of the digital world, research should be focused on rapid estimates of head pose angles and overcome the problems of lighting conditions [7], blurring [8], occultation [9], or environment conditions [10].…”

Section: Introductionmentioning

confidence: 99%

Attention Span Prediction Using Head-Pose Estimation With Deep Neural Networks

Singh

Mohadikar

Gite³

et al. 2021

IEEE Access

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Automated human pose estimation is evolving as an exciting research area in human activity detection. It includes sophisticated applications such as malpractice detection in the examination, distracted driving, gesture detection, etc., and requires robust and reliable pose estimation techniques. These applications help to map the attention of the user with head pose estimation (HPE) metrics supported by emotion and gaze analysis. This paper solves the problem of attention score estimation with HPE. The proposed method ensures ease of implementation while addressing head pose estimation using 68 facial features. Further, to attain reliability and precision, head pose estimation has been implemented as a regression task. The coordinate pair angle method (CPAM) with deep neural network (DNN) regression and elastic net regression is carried out. The use of DNN ensures precision on low lighting, distorted or occluded images. CPAM methodology leverages facial landmark detection and angular difference to estimate head pose. Experimentation results showed that the proposed model could handle large datasets, real-time data processing, significant pose variations, partial occlusions, and diverse facial expressions with a mean absolute error (MAE) of 3° and less. The proposed system was evaluated on three standard databases: the 300W across large poses (300W-LP) dataset, annotated facial landmarks in the wild (AFLW2000) dataset, and the national institute of mental health child emotional faces picture set (NIMH-ChEFS) dataset. The results achieved are on par with recent state-of-the-art methodologies such as anisotropic angle distribution learning (AADL), joint head pose estimation and face alignment algorithm (JFA), rotation axis focused attention network (RAFA-Net), and propose an MAE ranging up to 6°. The paper could achieve remarkable results for attention span prediction using head pose estimation and for many possible future applications.

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Virtual Portraitist

Hsieh

Lin

et al. 2019

ACM Trans. Multimedia Comput. Commun. Appl.

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Smart photography carries the promise of quality improvement and functionality extension in making aesthetically appealing pictures. In this article, we focus on self-portrait photographs and introduce new methods that guide a user in how to best pose while taking a selfie. While most of the current solutions use a post processing procedure to beautify a picture, the developed tool enables a novel function of recommending a good look before the photo is captured. Given an input face image, the tool automatically estimates the pose-based aesthetic score, finds the most attractive angle of the face, and suggests how the pose should be adjusted. The recommendation results are determined adaptively to the appearance and initial pose of the input face. We apply a data mining approach to find distinctive, frequent itemsets and association rules from online profile pictures, upon which the aesthetic estimation and pose recommendation methods are developed. A simulated and a real image set are used for experimental evaluation. The results show the proposed aesthetic estimation method can effectively select user-favorable photos. Moreover, the recommendation performance for the vertical adjustment is moderately related to the degree of conformity among the professional photographers’ recommendations. This study echoes the trend of instant photo sharing, in which a user takes a picture and then immediately shares it on a social network without engaging in tedious editing.

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Head Pose Recommendation for Taking Good Selfies

Cited by 4 publications

References 20 publications

Recognition of Human Face Emotions Detection Using Computer Vision Based Smart Images

Recognition of Human Face Emotions Detection Using Computer Vision Based Smart Images

Attention Span Prediction Using Head-Pose Estimation With Deep Neural Networks

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