Unconstrained face identification, facial periocular recognition, facial land marking and pose prediction, facial expression recognition, 3D facial model design, and other facial -related problems require robust face detection in the wild. Despite the fact that the face recognition issue has been researched intensively for decades with diff erent commercial implementations, it nevertheless faces problems in certain real -world scenarios due to multiple obstacles, such as severe facial occlusions, incredibly low resolutions, intense lighting, exceptionally pose inconsistencies, picture or video compression artefacts, and so on. To solve the problems described above, a face detection technique called Convolution Neural Network with Constant Error Carousel dependent Long Short Term Memory (CNN -CEC-LSTM) is proposed in this paper. This research imp lemented a novel network structure and designed a special feature extraction that employs a self -channel attention (SCA) block and a self -spatial attention (SSA) block that adaptively aggregates the feature maps in both channel and spatial domains to learn the inter -channel and inter -spatial connection matrices; additionally, matrix multiplications are conducted for a This approach first smoothed the initial image with a Gaussian filter before measuring th e gradient image. The Canny -Kirsch Method edge detec tion algorithm was then used to identify human face edges. The proposed method is evaluated against two recent difficult face detection databases, including the IIT Kanpur Dataset. The experimental findings indicate that the proposed approach outperforms t he most current cutting -edge face recognition approaches.