Analysis of Query Optimization Techniques in Databases

Mor, Jyoti; Kashyap, Indu; Rathy, R. K.

doi:10.5120/7262-0127

Cited by 5 publications

(1 citation statement)

References 8 publications

(7 reference statements)

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“…Optical flow allows for the recovery of geometric aspects of facial emotions and movement features, while wavelet decomposition minimizes noise. For typical ML approaches, such as DL methods, significant computational and memory resources are unnecessary, leading to the creation of embedded devices that can classify in real-time with minimal resources and produce appropriate outcomes [21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Existing Systemsmentioning

confidence: 99%

Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques

Rajasekhar,

Sreekar,

Revanth

et al. 2023

E3S Web Conf.

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To make machines more human-like, computer vision is essential. Computer vision is a field that focuses on mimicking the capabilities of the human visual framework to capture high-level understanding from enhanced images or recordings. Such a computer vision application gives the machine the ability to recognize a person and detect their emotions in order to process them appropriately. Facial verification can be a process of identifying or validating an object through an image, video, or any audio-visual component of their face. It could be a biometric discriminant proof strategy that works directly and confronts measures of discriminating individuals through their facial design and biometric information. The innovation collects a unique set of biometric information from each individual regarding their face and facial expressions to identify an individual. Facial sensory recognition can be an innovation used to analyse estimates from a variety of sources, such as images and recordings. It makes a difference as machines get better the way humans do and treat them according to their emotions. We are using a deep learning computation called Convolutional Neural Networks (CNN) to prepare for this demonstration that determines the sentiment of certain input images. We need to pre-process the images to prepare and test the model. For pre-processing, we do image enhancement combining resizing, equalizing and converting the image to grayscale for the machine to achieve. This demonstration can have multiple applications in both surveillance and feedback systems.

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