muhtasin-mashrur-adit Goto Github PK

4-way_traffic_control_system

ad

automatic-vehicle-number-plate-recongnition

bencoref

Codes related to the dataset and its associated paper

bttr

Official implementation for ICDAR 2021 best poster paper "Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer"

c_program_practise_competetive

car_rental_system

cleaner-360

gender-and-age-prediction-using-neural-network-and-machine-learning

Age and gender, 2 of the key facial attributes, play a really foundational role in social interactions, creating age and gender estimation from a single face image a crucial task in intelligent applications, like access management, human-computer interaction etc. Advancement in computer vision makes this prediction even more practical and open to all, thus enabling the world to come up with datasets, one of which, used in this paper, is UTK-Face that has 23705 pictures of male and female ageing from 0 to 116. In this paper, we propose a Convolution Neural Network (CNN) to predict age and gender. CNN is a Neural Network (NN) algorithm that extracts the deep features from the image and specifies the desired output at the final layers. Age prediction is approximately near to the real values with a five difference in both ways. Gender prediction is accurate in all the test data presented to the model. Validating with arguments shows no change in training and validation. Our model successfully executed approximately 80% in gender prediction and 60% in age prediction that can be furtherly advanced with pipelining with other classification models and much larger real-world dataset

school_management_system

software_digitization_service

start-up_success_rate

traffic_control_system