Real-Time Age Estimation from Facial Images Using YOLO and EfficientNet

Castellano, Giovanna; Carolis, Berardina De; Marvulli, Nicola; Sciancalepore, Mauro; Vessio, Gennaro

doi:10.1007/978-3-030-89131-2_25

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Among the methods, YOLO-V5 base Attendance system [43] exhibits robust accuracy at the basic frame level (97.5%), but its performance falters in more challenging scenarios (76%). Similarly, YOLO-V5 based Live human detection [50] method achieves commendable accuracy at the basic level (95.6%) but faces notable difficulties in handling tough frames (68%). While the Structure of multi-patterns with YOLO-V5 [51] demonstrates promising results at the basic level (93.1%), it reveals limitations in more complex scenarios (80%).…”

Section: B Face Recognition Accuracy Results Of Proposed Model At Gpumentioning

confidence: 99%

Deep Learning Based Multi Pose Human Face Matching System

Sohail,

Shoukat,

Khan

et al. 2024

IEEE Access

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Current techniques for multi-pose human face matching yield suboptimal outcomes because of the intricate nature of pose equalization and face rotation. Deep learning models, such as YOLO-V5, etc., that have been proposed to tackle these complexities, suffer from slow frame matching speeds and therefore exhibit low face recognition accuracy. Concerning this, certain literature investigated multi-pose human face detection systems; however, those studies are of elementary level and do not adequately analyze the utility of those systems. To fill this research gap, we propose a real-time face matching algorithm based on YOLO-V5. Our algorithm utilizes multi-pose human patterns and considers various face orientations, including organizational faces and left, right, top, and bottom alignments, to recognize multiple aspects of people. Using face poses, the algorithm identifies face positions in a dataset of images obtained from mixed pattern live streams, and compares faces with a specific piece of the face that has a relatively similar spectrum for matching with a given dataset. Once a match is found, the algorithm displays the face on Google Colab, collected during the learning phase with the Robo-flow key, and tracks it using the YOLO-V5 face monitor. Alignment variations are broken up into different positions, where each type of face is uniquely learned to have its own study demonstrated. This method offers several benefits for identifying and monitoring humans using their labeling tag as a pattern name, including high face-matching accuracy and minimum speed of owing face-to-pose variations. Furthermore, the algorithm addresses the face rotation issue by introducing a mixture of error functions for execution time, accuracy loss, frame-wise failure, and identity loss, attempting to guide the authenticity of the produced image frame. Experimental results confirm effectiveness of the algorithm in terms of improved accuracy and reduced delay in the face-matching paradigm.

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Section: B Face Recognition Accuracy Results Of Proposed Model At Gpumentioning

confidence: 99%

Deep Learning Based Multi Pose Human Face Matching System

Sohail,

Shoukat,

Khan

et al. 2024

IEEE Access

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“…Te module employs an automatic speech recognition model to extract raw text input from the user's voice. Moreover, we adopt the facial recognition network outlined in [13] and subsequently develop an expression recognition network to determine the user's emotional state from the given input image. It is worth noting that vocal intonations are signifcant carriers of emotion.…”

Section: Systemmentioning

confidence: 99%

“…For our robust facial recognition capabilities, we harnessed the power of a groundbreaking single-shot object detection model, YOLO ("You Only Look Once") [13]. YOLO distinctively deviates from the constraints of conventional region-based detectors, pioneering a grid-based detection strategy.…”

Section: Face Recognitionmentioning

confidence: 99%

Beyond Words: An Intelligent Human-Machine Dialogue System with Multimodal Generation and Emotional Comprehension

Zhao,

Cheng,

Huang

et al. 2023

International Journal of Intelligent Systems

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Intelligent service robots have become an indispensable aspect of modern-day society, playing a crucial role in various domains ranging from healthcare to hospitality. Among these robotic systems, human-machine dialogue systems are particularly noteworthy as they deliver both auditory and visual services to users, effectively bridging the communication gap between humans and machines. Despite their utility, the majority of existing approaches to these systems primarily concentrate on augmenting the logical coherence of the system’s responses, inadvertently neglecting the significance of user emotions in shaping a comprehensive communication experience. To tackle this shortcoming, we propose the development of an innovative human-machine dialogue system that is both intelligent and emotionally sensitive, employing multimodal generation techniques. This system is architecturally comprised of three components: (1) data collection and processing, responsible for gathering and preparing relevant information, (2) a dialogue engine, which generates contextually appropriate responses, and (3) an interaction module, responsible for facilitating the communication interface between users and the system. To validate our proposed approach, we have constructed a prototype system and conducted an evaluation of the performance of the core dialogue engine by utilizing an open dataset. The results of our study indicate that our system demonstrates a remarkable level of multimodal generation response, ultimately offering a more human-like dialogue experience.

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Guess the Age 2021: Age Estimation from Facial Images with Deep Convolutional Neural Networks

Greco

2021

Computer Analysis of Images and Patterns

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Real-Time Age Estimation from Facial Images Using YOLO and EfficientNet

Cited by 9 publications

References 26 publications

Deep Learning Based Multi Pose Human Face Matching System

Deep Learning Based Multi Pose Human Face Matching System

Beyond Words: An Intelligent Human-Machine Dialogue System with Multimodal Generation and Emotional Comprehension

Guess the Age 2021: Age Estimation from Facial Images with Deep Convolutional Neural Networks

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