Facial expression recognition using fuzzified Pseudo Zernike Moments and structural features

Ahmady, Maryam; Mirkamali, Seyed Saeid; Pahlevanzadeh, Bahareh; Pashaei, Elnaz; Hosseinabadi, Ali Asghar Rahmani; Słowik, Adam

doi:10.1016/j.fss.2022.03.013

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Genetic algorithms (GAs) are a class of generalpurpose search strategies based on natural selection and genetics. GAs have been used effectively to a wide array of optimization challenges [19,25,26]. In contrast to local search algorithms such as SA, which are often focused on manipulating a single viable solution and are extremely quick, GAs retain and modify a population of possible solutions.…”

Section: The Genetic Algorithm-based Methodsmentioning

confidence: 99%

Multiobjective Parallel Algorithms for Solving Biobjective Open Shop Scheduling Problem

2022

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Open Shop Scheduling Problem (OSSP) is one of the most important scheduling problems in the field of engineering and industry. This kind of problem includes m machines and n jobs, each job contains a certain number of operations, and each operation has a predetermined processing time on its corresponding machine. The order of processing of these operations affects the completion times of all jobs. Therefore, the purpose of OSSP is to achieve a proper order of processing of jobs using specified machines, so that the completion time of all jobs is minimized. In this paper, the strengths and limitations of three methods are evaluated by comparing the results of solving the OSSP in large-scale and small-scale benchmarks. In this case, the minimized completion time and total tardiness are considered the objective functions of the adapted methods. To solve small-scale problems, we adapt a mathematical model called Multiobjective Mixed Linear Programming (MOMILP). To solve large-scale problems, two metaheuristic algorithms including Multiobjective Parallel Genetic Algorithm (MOPGA) and Multiobjective Parallel Simulated Annealing (MOPSA) are adapted. In experimental results, we randomly generated small-scale problems to compare MOMILP with the Genetic Algorithm (GA) and Simulate Annealing (SA). To compare MOPSA and MOPGA with the state of the art and show their strengths and limitations, we use a standard large-scale benchmark. The simulation results of the proposed algorithms show that although the MOPSA algorithm is faster, the MOPGA algorithm is more efficient in achieving optimal solutions for large-scale problems compared with other methods.

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Section: The Genetic Algorithm-based Methodsmentioning

confidence: 99%

Multiobjective Parallel Algorithms for Solving Biobjective Open Shop Scheduling Problem

2022

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“…FR is the system by which an individual is recognized by comparing live capture or digital image data with that individual's stored record [5], [6]. Images with faces in them are crucial for intelligent vision-based human-computer interaction (HCI), prompting researchers to focus on tasks including face tracking, emotion identification, facial posture estimation, and facial recognition [7], [8]. Progress achieved in detecting and recognizing still images and videos using frontal view, lateral face view, or facial emotions, including dissatisfaction, happiness, and gloominess [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Facial recognition based on enhanced neural network

AL-Qinani,

Saleh,

Saleh

2024

IJ-AI

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Accurate automatic face recognition (FR) has only become a practical goal of biometrics research in recent years. Detection and recognition are the primary steps for identifying faces in this research, and The Viola-Jones algorithm implements to discover faces in images. This paper presents a neural network solution called modify bidirectional associative memory (MBAM). The basic idea is to recognize the image of a human's face, extract the face image, enter it into the MBAM, and identify it. The output ID for the face image from the network should be similar to the ID for the image entered previously in the training phase. The tests have conducted using the suggested model using 100 images. Results show that FR accuracy is 100% for all images used, and the accuracy after adding noise is the proportions that differ between the images used according to the noise ratio. Recognition results for the mobile camera images were more satisfactory than those for the Face94 dataset.

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“…Users can access the stored data at any time through any internet-connected terminal equipment using the application programming interface (API) provided by cloud providers. The immense growth of social media, ecommerce traffic and various web services has significantly raised the need for computational services [1]. One of the heuristically approaches of optimization for action extraction is ant colony optimization (ACO) along with particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

A hybrid facial features extraction-based classification framework for typhlotic people

Chopparapu,

Seventline Joseph

2024

Bulletin EEI

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Facial features play a vital role in the real-time cloud-based applications. Since, most of the conventional models are difficult to detect heterogeneous facial features due to high computational memory and time for the internet of things (IoT) based video surveillance mechanisms. Video based facial features identification and extraction include a large number of candidates features which are difficult to detect the contextual similarity of the facial key points due to noise and computational memory. In order to resolve these issues, a hybrid multiple features extraction measures are implemented on the real-time video dataset to extract key points using the cloud-based classifier. In this work, a hybrid classifier is used to classify the key facial points in the cloud computing environment. Experimental results show that the proposed hybrid multiple feature extraction-based frameworks have better computational efficiency in terms of error rate, recall, precision, and accuracy than the conventional models.

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Facial expression recognition using fuzzified Pseudo Zernike Moments and structural features

Cited by 6 publications

References 24 publications

Multiobjective Parallel Algorithms for Solving Biobjective Open Shop Scheduling Problem

Multiobjective Parallel Algorithms for Solving Biobjective Open Shop Scheduling Problem

Facial recognition based on enhanced neural network

A hybrid facial features extraction-based classification framework for typhlotic people

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