Automated Face Detection, Recognition and Gender Estimation Applied to Person Identification

Hannan,; Islam, Rafiqul; Haque, A S M Farhan Al; Hossain, S. M. E.; Ulhaq, Anwaar; Sawan, Jeyson Jaman

doi:10.3844/jcssp.2019.395.415

Cited by 4 publications

(2 citation statements)

References 24 publications

(30 reference statements)

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“…Tis means that disagreeing picture mountaineering has no infuence on the set of key places preferred. Again, handcrafted feature-based approaches accumulate difering facets from face photos to build forceful feature headings that were used earlier to train classifers to a degree, such as SVM, LDA, and BPNN [7,8]. Tese methods examine the textural, picturevalue, and motion-located traits of face photographs to equate original and fake face photos.…”

Section: Related Workmentioning

confidence: 99%

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Learning Condition-Invariant Scene Representations for Place Recognition across the Seasons Using Auto-Encoder and ICA

Islam

Rahman

2022

Journal of Electrical and Computer Engineering

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To localize the position and perfect autonomous navigation, building up a map is essential for mobile robots. The map becomes very important when the weather is not appropriate for the robot. However, the map becomes inconsistent when the robot moves in the environment and detects errors with emotional accuracy. The loop-closure detection is the process through which a robot can acknowledge the location visited previously, which can identify the ultimate solution to the previous problem. The robot faced difficulty identifying its previously visited path when the environment underwent an extreme change. The main motive of our work is to promote a model capable of understanding the scenes that are presented robustly. Moreover, during seasonal changes, this model provides an appropriate loop-closure detection result. Independent component analysis (ICA) and auto-encoder are proposed to complete our research work. ICA is a powerful tool to describe invariant images perfectly. Especially, when the robot moves through a changing environment, ICA provided more accurate outcomes than the other algorithm (baseline algorithm). On the other hand, the auto-encoder can distinguish between two features of scene variant condition and invariant condition. The encoder takes our work’s next steps by discovering possible routes. To analyze the performance, this work uses the baseline method with a precision-recall curve and a fraction of correct matches. The proposed algorithm ICA showed a 91.05% accuracy rate, which is better than the baseline algorithms, and the appropriate route-finding rate using an auto-encoder is also acceptable.

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Section: Related Workmentioning

confidence: 99%

“…W p+1 ⟵ W p+1 /‖W p+1 ‖. (7) end while (8) return w p (9) end for (10) return W � [w 1 ; w 2 ; w 3 ; . .…”

Section: Outcomes Of the Spring-fall Datasetmentioning

confidence: 99%

Learning Condition-Invariant Scene Representations for Place Recognition across the Seasons Using Auto-Encoder and ICA

Islam

Rahman

2022

Journal of Electrical and Computer Engineering

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Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition

Islam

Hasib

Rahman

et al. 2022

Hum-Cent Intell Syst

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Building up a map is essential for mobile robots to localize their position and perfect autonomous navigation which is known as Simultaneous Localization and Mapping (SLAM). The map has become very important when the weather is inappropriate for the robot. However, the map becomes inconsistent when the robot moves in the environment and detects errors in its detection accuracy. The robot had difficulty identifying its previously visited path, which is called loop-closure detection when the climate changed immensely e.g. seasonal changes. The main goal of this work is to apply Independent Component Analysis (ICA) and Auto-Encoder (Convolutional Auto-Encoder and Fundamental Auto-Encoder) to understand the route through the robot. During the operation of robots across a wide range of environmental changing conditions, the ICA has auspicious potential to extract descriptors of condition-invariant images. On the other hand, Auto-Encoder has the capability to differentiate condition variant and condition invariant characteristics of a site and identify the most possible route for the robot. In order to complete this work perfectly, we used three seasonal datasets, they are Summer–Fall, Spring–Fall, and Summer–Spring datasets. This work uses the baseline method with a precision-recall curve and evaluates the performance of our proposed algorithm, especially the ICA algorithm. In short, the proposed algorithm ICA showed a 91.05% accuracy rate which is better than the baseline algorithm.

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Big Data and Artificial Intelligence (AI) to Detect Glaucoma

Yang

Xie

Islam

et al. 2022

2022 9th International Conference on Behavioural and Social Computing (BESC)

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Automated Face Detection, Recognition and Gender Estimation Applied to Person Identification

Cited by 4 publications

References 24 publications

Learning Condition-Invariant Scene Representations for Place Recognition across the Seasons Using Auto-Encoder and ICA

Learning Condition-Invariant Scene Representations for Place Recognition across the Seasons Using Auto-Encoder and ICA

Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition

Big Data and Artificial Intelligence (AI) to Detect Glaucoma

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