To improve visualisation, a novel low light image enhancement method is presented based on the non‐uniform illumination prior model. Images captured under low‐light conditions are often of low visibility. The suggested algorithm results in higher accuracy (99.06%), higher precision (99.12%), higher recall (99.07%), and higher specificity (99.10%) than the comparison algorithms. The experimental results show that the proposed method achieves superiority over other algorithms according to all parameters. The proposed system has been evaluated using three datasets of face images (SDUMLA-HMT, 113, and CASIA) via performance metrics of accuracy, precision, sensitivity, specific-ity, and time.

The proposed recognition method was tested with different three standard machine learning algorithms (Decision Tree (DT), K Nearest Neighbor(KNN), Support Vector Machine (SVM)). The system can be trained to recognize a set of people and to learn via an online method, by integrating the new people it processes and improving its predictions on the ones it already has. These features allow us to compare faces between them in an efficient way. DCNN is used to extract relevant facial features. The developed system uses deep convolutional neural networks (DCNN) because of the dominant representation there are some conditions including occlusions, expressions, illuminations, and pose, which can affect the deep FR performance. The goal of this paper is to develop a complete FR system using transfer learning in fog computing and cloud computing. FR is developing technology with multiple real-time applications. Many scientists and engineers around the world have focused on establishing increasingly robust and accurate algorithms and methods for these types of systems and their applications in everyday life. The development of biometric applications, such as facial recognition (FR), has recently become important in smart cities. Berdasarkan pengujian dan analisis, ditemukan bahwa mayoritas sistem mengalami peningkatan tingkat pengenalan dengan diterapkannya metode low-light image enhancement, dan sebagai metode terbaik HE (76,28866 %) menunjukkan hasil yang paling signifikan, disusul dengan AMSR (75,25773 %), MSR (74,2268 %), SSR (69,07216 %), BPDHE (67,01031 %) dan Dong (63,91753 %). Sebagai tujuan, keseluruhan sistem pengenalan wajah akan dibandingkan tingkat pengenalannya untuk menemukan metode low-light image enhancement terbaik. Dataset SOF dipilih sebagai target pengujian dikarenakan berisi citra dengan kondisi pencahayaan berbeda. Untuk menemukan metode low light image enhancement yang baik maka pada penelitian ini dirancang beberapa sistem pengenalan wajah dengan PCA sebagai ekstraksi fitur serta menerapkan SSR, MSR, AMSR, Dong, HE dan BPDHE sebagai metode low-light image enhancement. Terdapat banyak sekali metode low-light image enhancement yang tersedia, namun mana teknik yang lebih baik dalam pengenalan wajah masih menjadi perdebatan.

Maka low-light image enhancement dapat menjadi solusinya. Kondisi pencahayaan terjadi akibat pancaran yang diterima objek tidak mencukupi sehingga cenderung memiliki visibilat rendah, kontras berkurang, warna kabur, dan detail yang kabur.

Salah satu masalah yang dianggap umum adalah kokndisi pencahayaan. P class='bodyabstract'> Sistem pengenalan wajah secara umum akan digunakan secara real time dalam mengenali individu, artinya noise tidak dapat terhindarkan.

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