Spliced images detection by using Viola-Jones algorithms method

Prasanna, G.V. Sai; Pavani, K.; Singh, Mahesh K.

doi:10.1016/j.matpr.2021.06.300

Cited by 18 publications

(17 citation statements)

References 8 publications

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“…The suggested FERT scheme is shown in Figure 1. First, the face is detected from the input image sequence, using a variant of the Viola and Jones method [44]. The detected face, denoted, is resized to using bi-cubic greyscale interpolation.…”

Section: Proposed Facial Emotion Recognition Techniquementioning

confidence: 99%

Recognizing facial emotions for educational learning settings

Kingsley

Inyang

Msugh

et al. 2022

IJRA

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Educational learning settings exploit cognitive factors as ultimate feedback to enhance personalization in teaching and learning. But besides cognition, the emotions of the learner which reflect the affective learning dimension also play an important role in the learning process. The emotions can be recognized by tracking explicit behaviors of the learner like facial or vocal expressions. Despite reasonable efforts to recognize emotions, the research community is currently constraints by two issues, namely : i) the lack of efficient feature descriptors to accurately represent and prospectively recogniz e (detecting) the emotions of the learner ; ii) lack of contextual datasets to benchmark performances of emotion recognizers in the learning - speci fic scenarios, resulting in poor generalizations. This paper presents a facial emotion recognition technique (FERT). The FERT is realized through results of preliminary analysis across various facial feature descriptors. Emotions are classified using the m ultiple kernel learning (MKL) method which reportedly possesses good merits. A contextually relevant simulated learning emotion ( SLE ) dataset is introduced to validate the FERT scheme. Recognition performance of the FERT scheme generalizes to 90.3% on the SLE dataset. On more popular but noncontextually datasets, the scheme achi e ved 90.0% and 82.8% respectively extended Cohn Kanade (CK+) and acted facial expressions in the wild ( AFEW ) datasets. A test for the null hypothesis that there is no significant difference in the performances accuracies of the descriptors rather proved otherwise ( x2 = 14 . 619 , df = 5 , p = 0 . 01212 ) for a model considered at a 95% confidence interval.

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Section: Proposed Facial Emotion Recognition Techniquementioning

confidence: 99%

Recognizing facial emotions for educational learning settings

Kingsley

Inyang

Msugh

et al. 2022

IJRA

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“…Spectral aspects of the spoken signal are included in the acoustic level of analysis. The fundamental and resonant frequencies are influenced by factors such as the size of the vocal tract and the length and mass of the vocal folds 19) .…”

Section: Related Work For Speaker Identification Systemmentioning

confidence: 99%

Acoustic Features Extraction of Non-Electronic Disguised Voice for Speaker Identification

Kumar¹,

Kishore²,

Kiran³

2022

Evergreen

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A lot of challenges remain despite the rapid advancement of speaker identification technology for non-electronic disguised or physically disguised voices. This is one of the most difficult components of voice disguised that has yet to be overcome by the experts. With a normal or ideal voice, the identification process is simpler and leads to a more conclusive result. A problem arises when a suspect is identified using speech samples that have been altered to hide their identity. There are both unintended and intentional disguises in this sample content. This way a person speaks can be temporarily altered by stress, anger, concern, anxiousness, or sorrow. It is common for people to disguise themselves when they get unidentified, payment or threat phone calls. The speaker changes their voice purposefully fear of being discovered. This manuscript aims to identify a speaker for multimedia applications using a non-electronically disguised voice. In speech signal processing applications requiring non-electronic disguised voice under physical speech fluctuation, it is challenging to identify the speaker. The primary goal of this study was to regulate whether or not it was conceivable to prompt precise sentiments under disguised speech circumstances. Auditory and spectrographic investigations were used to determine the impact of different disguises on personal identity and speaker recognition performances.

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“…This paper also provides the average behavior circuit model. Parameter design of flying capacitor boost converter has been discussed in [12][13][14]. A comparative analysis of different DC-DC converter topologies has been discussed in [13].…”

Section: ░ 1 Introductionmentioning

confidence: 99%

“…Flying capacitor three level bipolar bidirectional DC-DC converter and its modulation strategy has been proposed in [12,[18][19][20]. Softswitched asymmetrical flying capacitor boost converter with synchronous rectification has been proposed in [14]. Design and control aspects of 3-level boost converter under discontinuous conduction mode has been presented in [15][16][17][18].…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Analysis of Flying Capacitor Boost Converter

Nishanta

Lavanya

2022

IJEER

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This paper analyzes the working principle of flying capacitor boost converter and its different variants such as synchronous flying capacitor boost converter and n-level flying capacitor boost converter. The circuit diagram and analysis of different waveforms have been provided. Voltage conversion ratio of different converters have been provided. The lower voltage conversion ratio (VCR), higher voltage stress, and low efficiency of the boost converter at higher duty cycle levels are the primary limitations of the device. Magnetic coupling components are employed to boost the VCR, but the rating is reduced as a result. The leakage current stored in the magnetic component causes unwanted voltage spikes to occur in the switches as a result of the leakage current. For the purpose of addressing these difficulties, several active and passive clamping switches are employed. Three-level boost converters are an excellent alternative to conventional boost converters, but they have a number of disadvantages that must be considered before using them.

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Spliced images detection by using Viola-Jones algorithms method

Cited by 18 publications

References 8 publications

Recognizing facial emotions for educational learning settings

Recognizing facial emotions for educational learning settings

Acoustic Features Extraction of Non-Electronic Disguised Voice for Speaker Identification

Analysis of Flying Capacitor Boost Converter

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